{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64c956bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1682ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the LEMCell\n",
    "class LEMCell(nn.Module):\n",
    "    def __init__(self, ninp, nhid, dt):\n",
    "        super(LEMCell, self).__init__()\n",
    "        self.ninp = ninp\n",
    "        self.nhid = nhid\n",
    "        self.dt = dt\n",
    "        self.inp2hid = nn.Linear(ninp, 4 * nhid)\n",
    "        self.hid2hid = nn.Linear(nhid, 3 * nhid)\n",
    "        self.transform_z = nn.Linear(nhid, nhid)\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        std = 1.0 / np.sqrt(self.nhid)\n",
    "        for w in self.parameters():\n",
    "            w.data.uniform_(-std, std)\n",
    "\n",
    "    def forward(self, x, y, z):\n",
    "        transformed_inp = self.inp2hid(x)\n",
    "        transformed_hid = self.hid2hid(y)\n",
    "        i_dt1, i_dt2, i_z, i_y = transformed_inp.chunk(4, 1)\n",
    "        h_dt1, h_dt2, h_y = transformed_hid.chunk(3, 1)\n",
    "\n",
    "        ms_dt_bar = self.dt * torch.sigmoid(i_dt1 + h_dt1)\n",
    "        ms_dt = self.dt * torch.sigmoid(i_dt2 + h_dt2)\n",
    "\n",
    "        z = (1. - ms_dt) * z + ms_dt * torch.tanh(i_y + h_y)\n",
    "        y = (1. - ms_dt_bar) * y + ms_dt_bar * torch.tanh(self.transform_z(z) + i_z)\n",
    "\n",
    "        return y, z\n",
    "\n",
    "# Define the LEM model\n",
    "class LEM(nn.Module):\n",
    "    def __init__(self, ninp, nhid, nout, dt=1.):\n",
    "        super(LEM, self).__init__()\n",
    "        self.nhid = nhid\n",
    "        self.cell = LEMCell(ninp, nhid, dt)\n",
    "        self.classifier = nn.Linear(nhid, nout)\n",
    "        self.init_weights()\n",
    "\n",
    "    def init_weights(self):\n",
    "        for name, param in self.named_parameters():\n",
    "            if 'classifier' in name and 'weight' in name:\n",
    "                nn.init.kaiming_normal_(param.data)\n",
    "\n",
    "    def forward(self, input):\n",
    "        y = input.data.new(input.size(1), self.nhid).zero_()\n",
    "        z = input.data.new(input.size(1), self.nhid).zero_()\n",
    "        for x in input:\n",
    "            y, z = self.cell(x, y, z)\n",
    "        out = self.classifier(y)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a982afa5",
   "metadata": {},
   "source": [
    "### PINN solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79da65b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x size (1, 256)\n",
      "t size (1, 100)\n",
      "u size (256, 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing data\n",
    "\n",
    "# Load the .mat file\n",
    "mat_data = scipy.io.loadmat('burg.mat')\n",
    "\n",
    "# Access the variables stored in the .mat file\n",
    "# The variable names in the .mat file become keys in the loaded dictionary\n",
    "x = mat_data['x']\n",
    "t = mat_data['t']\n",
    "u = mat_data['u1']\n",
    "\n",
    "# #Use the loaded variables as needed\n",
    "# print(\"x size\", x.shape)\n",
    "# print(\"t size\", t.shape)\n",
    "# print(\"u size\", u.shape)\n",
    "\n",
    "# # X, T = np.meshgrid(x, t)\n",
    "# # Define custom color levels\n",
    "# c_levels = np.linspace(np.min(u), np.max(u), 100)\n",
    "\n",
    "# # Plot the contour\n",
    "# plt.figure(figsize=(15, 5))\n",
    "# plt.contourf(T, X, u.T, levels=c_levels, cmap='coolwarm')\n",
    "# plt.xlabel('t')\n",
    "# plt.ylabel('x')\n",
    "# plt.title('Burgers')\n",
    "# plt.colorbar()  # Add a colorbar for the contour levels\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39c40163",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Exact Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1973575",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9967dbae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x size (256, 1)\n",
      "t size (100, 1)\n",
      "u size (256, 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing data\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import time\n",
    "import scipy.io\n",
    "\n",
    "# Load the .mat file\n",
    "mat_data = scipy.io.loadmat('burgers_shock.mat')\n",
    "\n",
    "# Access the variables stored in the .mat file\n",
    "# The variable names in the .mat file become keys in the loaded dictionary\n",
    "x = mat_data['x']\n",
    "t = mat_data['t']\n",
    "u_1 = mat_data['usol']\n",
    "\n",
    "# #Use the loaded variables as needed\n",
    "# print(\"x size\", x.shape)\n",
    "# print(\"t size\", t.shape)\n",
    "# print(\"u size\", u.shape)\n",
    "\n",
    "# X, T = np.meshgrid(x, t)\n",
    "# # Define custom color levels\n",
    "# c_levels = np.linspace(np.min(u_1), np.max(u_1), 100)\n",
    "\n",
    "# # Plot the contour\n",
    "# plt.figure(figsize=(15, 5))\n",
    "# plt.contourf(T, X, u_1.T, levels=c_levels, cmap='coolwarm')\n",
    "# plt.xlabel('t')\n",
    "# plt.ylabel('x')\n",
    "# plt.title('Burgers')\n",
    "# plt.colorbar()  # Add a colorbar for the contour levels\n",
    "# plt.savefig('Contour_Exact.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fdd0b99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10ae6b54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import torch\n",
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# # Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# # concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "\n",
    "\n",
    "# # Define custom color levels\n",
    "# X, T = np.meshgrid(x, t)\n",
    "\n",
    "# # Define custom color levels using the minimum and maximum from the NumPy array\n",
    "# c_levels = np.linspace(np.min(u_1), np.max(u_1), 100)\n",
    "\n",
    "# # Plot the contour\n",
    "# plt.figure(figsize=(15, 5))\n",
    "# CS1 = plt.contourf(T, X, u_1.T, levels=c_levels, cmap='coolwarm')\n",
    "\n",
    "# # Create a custom font with Times New Roman\n",
    "# plt.rcParams['figure.figsize'] = [10, 4]\n",
    "\n",
    "# from matplotlib.font_manager import FontProperties\n",
    "# plt.rcParams['axes.unicode_minus'] = False\n",
    "# font_path = 'times-new-roman.ttf'\n",
    "# custom_font = FontProperties(fname=font_path)\n",
    "\n",
    "# cbar1 = plt.colorbar(CS1)\n",
    "# # Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "# num_ticks = 5\n",
    "# c_ticks = np.linspace(np.min(u_1), np.max(u_1), num_ticks)\n",
    "# cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# for t in cbar1.ax.get_yticklabels():\n",
    "#     t.set_fontproperties(custom_font)\n",
    "#     t.set_fontsize(20)\n",
    "\n",
    "# plt.xlabel('$t$', fontsize=20, fontproperties=custom_font)\n",
    "# plt.ylabel('$x$', fontsize=20, fontproperties=custom_font)\n",
    "# plt.title('$u(x, t)$', fontsize=20, fontproperties=custom_font)\n",
    "# plt.xticks(fontsize=20, fontproperties=custom_font)\n",
    "# plt.yticks(fontsize=20, fontproperties=custom_font)\n",
    "\n",
    "# # Add a dotted line at t = 0.8\n",
    "# #plt.axvline(x=0.8, color='black', linestyle='dotted')\n",
    "\n",
    "# # Set the number of ticks for x-axis and y-axis to 5\n",
    "# num_ticks = 5\n",
    "# x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "# y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "# plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "# plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "# cbar1.locator = FixedLocator(c_ticks)\n",
    "\n",
    "# plt.savefig('Exact_burg.pdf', dpi=300)\n",
    "# plt.savefig('exact_burg', dpi=300)\n",
    "# # Show the plot\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83a01b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 100)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Toy problem data\n",
    "input_size = 256\n",
    "hidden_size = 32\n",
    "output_size = 256\n",
    "sequence_length = 79\n",
    "batch_size = 1\n",
    "num_epochs = 20000\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "u[:, 0:100].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0496e4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data shape (256,)\n",
      "input data shape (256, 79)\n",
      "Target data shape (256, 79)\n",
      "input tensor shape torch.Size([1, 79, 256])\n",
      "Target tensor shape torch.Size([1, 79, 256])\n"
     ]
    }
   ],
   "source": [
    "input_data = u[:,0:79]\n",
    "target_data = u[:,1:80]\n",
    "\n",
    "test_data = u[:,79]\n",
    "#test_target = u[:,80:100]\n",
    "\n",
    "print(\"test data shape\", test_data.shape)\n",
    "#print(\"test target shape\", test_target.shape)\n",
    "\n",
    "print(\"input data shape\",input_data.shape)\n",
    "print(\"Target data shape\",target_data.shape)\n",
    "\n",
    "# Convert data to tensors\n",
    "input_tensor = torch.tensor(input_data.T).view(batch_size, sequence_length, input_size).float()\n",
    "target_tensor = torch.tensor(target_data.T).view(batch_size, sequence_length, output_size).float()\n",
    "\n",
    "print(\"input tensor shape\",input_tensor.shape)\n",
    "print(\"Target tensor shape\",target_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "718d5b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert test data to tensors\n",
    "test_tensor = torch.tensor(test_data.T).view(batch_size, 1, input_size).float()\n",
    "#test_target_tensor = torch.tensor(test_target.T).view(batch_size, 20, output_size).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d733ab9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10/20000, Loss: 0.3866974115371704\n",
      "Epoch: 20/20000, Loss: 0.3487780690193176\n",
      "Epoch: 30/20000, Loss: 0.3087990283966064\n",
      "Epoch: 40/20000, Loss: 0.2699531018733978\n",
      "Epoch: 50/20000, Loss: 0.2344971448183060\n",
      "Epoch: 60/20000, Loss: 0.2015173286199570\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/anaconda3/lib/python3.9/site-packages/torch/nn/modules/loss.py:530: UserWarning: Using a target size (torch.Size([1, 79, 256])) that is different to the input size (torch.Size([79, 256])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 70/20000, Loss: 0.1723766177892685\n",
      "Epoch: 80/20000, Loss: 0.1483513861894608\n",
      "Epoch: 90/20000, Loss: 0.1284097582101822\n",
      "Epoch: 100/20000, Loss: 0.1117339953780174\n",
      "Epoch: 110/20000, Loss: 0.0977326631546021\n",
      "Epoch: 120/20000, Loss: 0.0859487056732178\n",
      "Epoch: 130/20000, Loss: 0.0760185718536377\n",
      "Epoch: 140/20000, Loss: 0.0676476433873177\n",
      "Epoch: 150/20000, Loss: 0.0605934187769890\n",
      "Epoch: 160/20000, Loss: 0.0546537898480892\n",
      "Epoch: 170/20000, Loss: 0.0496585704386234\n",
      "Epoch: 180/20000, Loss: 0.0454635843634605\n",
      "Epoch: 190/20000, Loss: 0.0419462285935879\n",
      "Epoch: 200/20000, Loss: 0.0390020199120045\n",
      "Epoch: 210/20000, Loss: 0.0365417525172234\n",
      "Epoch: 220/20000, Loss: 0.0344892106950283\n",
      "Epoch: 230/20000, Loss: 0.0327788665890694\n",
      "Epoch: 240/20000, Loss: 0.0313525013625622\n",
      "Epoch: 250/20000, Loss: 0.0301372334361076\n",
      "Epoch: 260/20000, Loss: 0.0290109999477863\n",
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      "Epoch: 280/20000, Loss: 0.0271436050534248\n",
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      "Epoch: 370/20000, Loss: 0.0210633613169193\n",
      "Epoch: 380/20000, Loss: 0.0205504018813372\n",
      "Epoch: 390/20000, Loss: 0.0200629048049450\n",
      "Epoch: 400/20000, Loss: 0.0195939000695944\n",
      "Epoch: 410/20000, Loss: 0.0191286448389292\n",
      "Epoch: 420/20000, Loss: 0.0186489038169384\n",
      "Epoch: 430/20000, Loss: 0.0181993953883648\n",
      "Epoch: 440/20000, Loss: 0.0177528280764818\n",
      "Epoch: 450/20000, Loss: 0.0173244308680296\n",
      "Epoch: 460/20000, Loss: 0.0169120337814093\n",
      "Epoch: 470/20000, Loss: 0.0165140908211470\n",
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      "Epoch: 530/20000, Loss: 0.0143658937886357\n",
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      "Epoch: 1950/20000, Loss: 0.0005177774582990\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1960/20000, Loss: 0.0005102361319587\n",
      "Epoch: 1970/20000, Loss: 0.0005028438754380\n",
      "Epoch: 1980/20000, Loss: 0.0004955942276865\n",
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      "Epoch: 2020/20000, Loss: 0.0004686535976361\n",
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      "Epoch: 2290/20000, Loss: 0.0003232590388507\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
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      "Epoch: 17990/20000, Loss: 0.0000002506156704\n",
      "Epoch: 18000/20000, Loss: 0.0000002511102366\n",
      "Epoch: 18010/20000, Loss: 0.0000002482412924\n",
      "Epoch: 18020/20000, Loss: 0.0000002468956097\n",
      "Epoch: 18030/20000, Loss: 0.0000002462979580\n",
      "Epoch: 18040/20000, Loss: 0.0000002455236938\n",
      "Epoch: 18050/20000, Loss: 0.0000002448996099\n",
      "Epoch: 18060/20000, Loss: 0.0000002442997982\n",
      "Epoch: 18070/20000, Loss: 0.0000002437101898\n",
      "Epoch: 18080/20000, Loss: 0.0000002431343660\n",
      "Epoch: 18090/20000, Loss: 0.0000002425728098\n",
      "Epoch: 18100/20000, Loss: 0.0000002420195528\n",
      "Epoch: 18110/20000, Loss: 0.0000002414742255\n",
      "Epoch: 18120/20000, Loss: 0.0000002409525166\n",
      "Epoch: 18130/20000, Loss: 0.0000002428058110\n",
      "Epoch: 18140/20000, Loss: 0.0000009083918826\n",
      "Epoch: 18150/20000, Loss: 0.0000031924587347\n",
      "Epoch: 18160/20000, Loss: 0.0000012930644289\n",
      "Epoch: 18170/20000, Loss: 0.0000007411633192\n",
      "Epoch: 18180/20000, Loss: 0.0000007166964906\n",
      "Epoch: 18190/20000, Loss: 0.0000002601866811\n",
      "Epoch: 18200/20000, Loss: 0.0000002746981522\n",
      "Epoch: 18210/20000, Loss: 0.0000002617381938\n",
      "Epoch: 18220/20000, Loss: 0.0000002424767445\n",
      "Epoch: 18230/20000, Loss: 0.0000002401905874\n",
      "Epoch: 18240/20000, Loss: 0.0000002396700154\n",
      "Epoch: 18250/20000, Loss: 0.0000002388530334\n",
      "Epoch: 18260/20000, Loss: 0.0000002380528485\n",
      "Epoch: 18270/20000, Loss: 0.0000002373876100\n",
      "Epoch: 18280/20000, Loss: 0.0000002367898020\n",
      "Epoch: 18290/20000, Loss: 0.0000002362270237\n",
      "Epoch: 18300/20000, Loss: 0.0000002356812132\n",
      "Epoch: 18310/20000, Loss: 0.0000002351502530\n",
      "Epoch: 18320/20000, Loss: 0.0000002346277626\n",
      "Epoch: 18330/20000, Loss: 0.0000002341168823\n",
      "Epoch: 18340/20000, Loss: 0.0000002336082900\n",
      "Epoch: 18350/20000, Loss: 0.0000002331072864\n",
      "Epoch: 18360/20000, Loss: 0.0000002326145960\n",
      "Epoch: 18370/20000, Loss: 0.0000002324345871\n",
      "Epoch: 18380/20000, Loss: 0.0000002835483883\n",
      "Epoch: 18390/20000, Loss: 0.0000114312879305\n",
      "Epoch: 18400/20000, Loss: 0.0000025969361559\n",
      "Epoch: 18410/20000, Loss: 0.0000007884903539\n",
      "Epoch: 18420/20000, Loss: 0.0000003721521011\n",
      "Epoch: 18430/20000, Loss: 0.0000003021075088\n",
      "Epoch: 18440/20000, Loss: 0.0000002624070987\n",
      "Epoch: 18450/20000, Loss: 0.0000002463918634\n",
      "Epoch: 18460/20000, Loss: 0.0000002415937104\n",
      "Epoch: 18470/20000, Loss: 0.0000002379914861\n",
      "Epoch: 18480/20000, Loss: 0.0000002341273273\n",
      "Epoch: 18490/20000, Loss: 0.0000002322395574\n",
      "Epoch: 18500/20000, Loss: 0.0000002315819216\n",
      "Epoch: 18510/20000, Loss: 0.0000002307643570\n",
      "Epoch: 18520/20000, Loss: 0.0000002301679700\n",
      "Epoch: 18530/20000, Loss: 0.0000002295551553\n",
      "Epoch: 18540/20000, Loss: 0.0000002289825858\n",
      "Epoch: 18550/20000, Loss: 0.0000002284274530\n",
      "Epoch: 18560/20000, Loss: 0.0000002278826940\n",
      "Epoch: 18570/20000, Loss: 0.0000002273521886\n",
      "Epoch: 18580/20000, Loss: 0.0000002268303803\n",
      "Epoch: 18590/20000, Loss: 0.0000002263183916\n",
      "Epoch: 18600/20000, Loss: 0.0000002258140626\n",
      "Epoch: 18610/20000, Loss: 0.0000002253196953\n",
      "Epoch: 18620/20000, Loss: 0.0000002248290656\n",
      "Epoch: 18630/20000, Loss: 0.0000002243440633\n",
      "Epoch: 18640/20000, Loss: 0.0000002238758015\n",
      "Epoch: 18650/20000, Loss: 0.0000002240531245\n",
      "Epoch: 18660/20000, Loss: 0.0000003083836475\n",
      "Epoch: 18670/20000, Loss: 0.0000126882741824\n",
      "Epoch: 18680/20000, Loss: 0.0000012452658211\n",
      "Epoch: 18690/20000, Loss: 0.0000006424186836\n",
      "Epoch: 18700/20000, Loss: 0.0000003395394117\n",
      "Epoch: 18710/20000, Loss: 0.0000002740376033\n",
      "Epoch: 18720/20000, Loss: 0.0000002588176358\n",
      "Epoch: 18730/20000, Loss: 0.0000002495994806\n",
      "Epoch: 18740/20000, Loss: 0.0000002366159606\n",
      "Epoch: 18750/20000, Loss: 0.0000002260173062\n",
      "Epoch: 18760/20000, Loss: 0.0000002236016172\n",
      "Epoch: 18770/20000, Loss: 0.0000002233006171\n",
      "Epoch: 18780/20000, Loss: 0.0000002221247826\n",
      "Epoch: 18790/20000, Loss: 0.0000002218937567\n",
      "Epoch: 18800/20000, Loss: 0.0000002401708059\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18810/20000, Loss: 0.0000019195299501\n",
      "Epoch: 18820/20000, Loss: 0.0000012127404716\n",
      "Epoch: 18830/20000, Loss: 0.0000003159397011\n",
      "Epoch: 18840/20000, Loss: 0.0000002833993733\n",
      "Epoch: 18850/20000, Loss: 0.0000002608619809\n",
      "Epoch: 18860/20000, Loss: 0.0000002210373253\n",
      "Epoch: 18870/20000, Loss: 0.0000002242726396\n",
      "Epoch: 18880/20000, Loss: 0.0000002176928575\n",
      "Epoch: 18890/20000, Loss: 0.0000002173640041\n",
      "Epoch: 18900/20000, Loss: 0.0000002166534330\n",
      "Epoch: 18910/20000, Loss: 0.0000002159750068\n",
      "Epoch: 18920/20000, Loss: 0.0000002154983889\n",
      "Epoch: 18930/20000, Loss: 0.0000002167432456\n",
      "Epoch: 18940/20000, Loss: 0.0000003848773531\n",
      "Epoch: 18950/20000, Loss: 0.0000135023719849\n",
      "Epoch: 18960/20000, Loss: 0.0000003910457451\n",
      "Epoch: 18970/20000, Loss: 0.0000006535237276\n",
      "Epoch: 18980/20000, Loss: 0.0000005575772661\n",
      "Epoch: 18990/20000, Loss: 0.0000004090118466\n",
      "Epoch: 19000/20000, Loss: 0.0000002753758963\n",
      "Epoch: 19010/20000, Loss: 0.0000002187123869\n",
      "Epoch: 19020/20000, Loss: 0.0000002218671966\n",
      "Epoch: 19030/20000, Loss: 0.0000002176680027\n",
      "Epoch: 19040/20000, Loss: 0.0000002152261658\n",
      "Epoch: 19050/20000, Loss: 0.0000002143334825\n",
      "Epoch: 19060/20000, Loss: 0.0000002137289954\n",
      "Epoch: 19070/20000, Loss: 0.0000002130270360\n",
      "Epoch: 19080/20000, Loss: 0.0000002124780849\n",
      "Epoch: 19090/20000, Loss: 0.0000002119646467\n",
      "Epoch: 19100/20000, Loss: 0.0000002114647515\n",
      "Epoch: 19110/20000, Loss: 0.0000002110574542\n",
      "Epoch: 19120/20000, Loss: 0.0000002164147048\n",
      "Epoch: 19130/20000, Loss: 0.0000009787868294\n",
      "Epoch: 19140/20000, Loss: 0.0000007813544016\n",
      "Epoch: 19150/20000, Loss: 0.0000003206840518\n",
      "Epoch: 19160/20000, Loss: 0.0000003877991048\n",
      "Epoch: 19170/20000, Loss: 0.0000002746238579\n",
      "Epoch: 19180/20000, Loss: 0.0000003852953512\n",
      "Epoch: 19190/20000, Loss: 0.0000038577682062\n",
      "Epoch: 19200/20000, Loss: 0.0000008726321425\n",
      "Epoch: 19210/20000, Loss: 0.0000008996830161\n",
      "Epoch: 19220/20000, Loss: 0.0000005013283726\n",
      "Epoch: 19230/20000, Loss: 0.0000002922107853\n",
      "Epoch: 19240/20000, Loss: 0.0000002393764191\n",
      "Epoch: 19250/20000, Loss: 0.0000002245950412\n",
      "Epoch: 19260/20000, Loss: 0.0000002126997742\n",
      "Epoch: 19270/20000, Loss: 0.0000002065943221\n",
      "Epoch: 19280/20000, Loss: 0.0000002075090180\n",
      "Epoch: 19290/20000, Loss: 0.0000002094391789\n",
      "Epoch: 19300/20000, Loss: 0.0000002377598776\n",
      "Epoch: 19310/20000, Loss: 0.0000010625399227\n",
      "Epoch: 19320/20000, Loss: 0.0000041256103032\n",
      "Epoch: 19330/20000, Loss: 0.0000003902756305\n",
      "Epoch: 19340/20000, Loss: 0.0000004613473834\n",
      "Epoch: 19350/20000, Loss: 0.0000004148189134\n",
      "Epoch: 19360/20000, Loss: 0.0000002514985908\n",
      "Epoch: 19370/20000, Loss: 0.0000002121711162\n",
      "Epoch: 19380/20000, Loss: 0.0000002067415750\n",
      "Epoch: 19390/20000, Loss: 0.0000002062097622\n",
      "Epoch: 19400/20000, Loss: 0.0000002051412906\n",
      "Epoch: 19410/20000, Loss: 0.0000002026050936\n",
      "Epoch: 19420/20000, Loss: 0.0000002027128261\n",
      "Epoch: 19430/20000, Loss: 0.0000002025910675\n",
      "Epoch: 19440/20000, Loss: 0.0000002082948072\n",
      "Epoch: 19450/20000, Loss: 0.0000003933478467\n",
      "Epoch: 19460/20000, Loss: 0.0000062127637648\n",
      "Epoch: 19470/20000, Loss: 0.0000025164124509\n",
      "Epoch: 19480/20000, Loss: 0.0000008425099054\n",
      "Epoch: 19490/20000, Loss: 0.0000002671143875\n",
      "Epoch: 19500/20000, Loss: 0.0000002124837977\n",
      "Epoch: 19510/20000, Loss: 0.0000002219949522\n",
      "Epoch: 19520/20000, Loss: 0.0000002148189395\n",
      "Epoch: 19530/20000, Loss: 0.0000002020890548\n",
      "Epoch: 19540/20000, Loss: 0.0000001994022938\n",
      "Epoch: 19550/20000, Loss: 0.0000001992909660\n",
      "Epoch: 19560/20000, Loss: 0.0000001998143802\n",
      "Epoch: 19570/20000, Loss: 0.0000002540388664\n",
      "Epoch: 19580/20000, Loss: 0.0000038587131712\n",
      "Epoch: 19590/20000, Loss: 0.0000018515227111\n",
      "Epoch: 19600/20000, Loss: 0.0000009184815326\n",
      "Epoch: 19610/20000, Loss: 0.0000006733751547\n",
      "Epoch: 19620/20000, Loss: 0.0000002123252187\n",
      "Epoch: 19630/20000, Loss: 0.0000002698793935\n",
      "Epoch: 19640/20000, Loss: 0.0000002051802568\n",
      "Epoch: 19650/20000, Loss: 0.0000002008910087\n",
      "Epoch: 19660/20000, Loss: 0.0000002010435480\n",
      "Epoch: 19670/20000, Loss: 0.0000001983139128\n",
      "Epoch: 19680/20000, Loss: 0.0000001968792844\n",
      "Epoch: 19690/20000, Loss: 0.0000001960843576\n",
      "Epoch: 19700/20000, Loss: 0.0000001954447697\n",
      "Epoch: 19710/20000, Loss: 0.0000001949169217\n",
      "Epoch: 19720/20000, Loss: 0.0000001944898997\n",
      "Epoch: 19730/20000, Loss: 0.0000001940465495\n",
      "Epoch: 19740/20000, Loss: 0.0000001936772236\n",
      "Epoch: 19750/20000, Loss: 0.0000001948068586\n",
      "Epoch: 19760/20000, Loss: 0.0000002823755665\n",
      "Epoch: 19770/20000, Loss: 0.0000077438662629\n",
      "Epoch: 19780/20000, Loss: 0.0000045950296226\n",
      "Epoch: 19790/20000, Loss: 0.0000008961662274\n",
      "Epoch: 19800/20000, Loss: 0.0000002052175603\n",
      "Epoch: 19810/20000, Loss: 0.0000003446493224\n",
      "Epoch: 19820/20000, Loss: 0.0000002452495949\n",
      "Epoch: 19830/20000, Loss: 0.0000001994794019\n",
      "Epoch: 19840/20000, Loss: 0.0000002017856104\n",
      "Epoch: 19850/20000, Loss: 0.0000001951989077\n",
      "Epoch: 19860/20000, Loss: 0.0000001927368345\n",
      "Epoch: 19870/20000, Loss: 0.0000001924666861\n",
      "Epoch: 19880/20000, Loss: 0.0000001922238368\n",
      "Epoch: 19890/20000, Loss: 0.0000002151378453\n",
      "Epoch: 19900/20000, Loss: 0.0000019855838218\n",
      "Epoch: 19910/20000, Loss: 0.0000011784313756\n",
      "Epoch: 19920/20000, Loss: 0.0000002159982273\n",
      "Epoch: 19930/20000, Loss: 0.0000002867578530\n",
      "Epoch: 19940/20000, Loss: 0.0000002127903969\n",
      "Epoch: 19950/20000, Loss: 0.0000001988168350\n",
      "Epoch: 19960/20000, Loss: 0.0000001940908021\n",
      "Epoch: 19970/20000, Loss: 0.0000002103947736\n",
      "Epoch: 19980/20000, Loss: 0.0000007034715281\n",
      "Epoch: 19990/20000, Loss: 0.0000064280652623\n",
      "Epoch: 20000/20000, Loss: 0.0000012396001239\n"
     ]
    }
   ],
   "source": [
    "# Create LEM instance\n",
    "lem = LEM(input_size, hidden_size, output_size, dt=0.1)\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(lem.parameters(), lr=0.001)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output = lem(input_tensor)\n",
    "    loss = criterion(output, target_tensor)\n",
    "\n",
    "    # Backward and optimize\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # Print progress\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f'Epoch: {epoch + 1}/{num_epochs}, Loss: {loss.item():.16f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1da66d64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 256])\n",
      "torch.Size([1, 20, 256])\n"
     ]
    }
   ],
   "source": [
    "print(test_tensor.shape)\n",
    "prediction_tensor = torch.zeros(1, 20, 256).float()\n",
    "print(prediction_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0543daa",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    prediction = lem(test_tensor)\n",
    "    prediction = prediction.view(1, 1, 256).float()\n",
    "    prediction_tensor[:, 0, :] = prediction\n",
    "    for i in range(19):\n",
    "        prediction = lem(prediction)\n",
    "        prediction = prediction.view(1, 1, 256).float()\n",
    "        prediction_tensor[:, i+1, :] = prediction\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6b9bad",
   "metadata": {},
   "source": [
    "### Four different types of error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62f97dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c33b0f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exact Solution\n",
    "\n",
    "u_test = u_1.T\n",
    "u_test_full = u_test[80:100, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fad72b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 256])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00c8fa22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 256)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_test_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "334bf0be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([20, 256])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extrapolation\n",
    "\n",
    "k1 = ( prediction_tensor - u_test_full)**2\n",
    "u_test_full_tensor = torch.tensor(u_test_full**2)\n",
    "u_test_full_tensor.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01080c4f",
   "metadata": {},
   "source": [
    "### L^2 norm error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33c17bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test:  0.00013050968844696258 %\n"
     ]
    }
   ],
   "source": [
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = torch.mean(k1)/ torch.mean(u_test_full_tensor)\n",
    "\n",
    "print(\"Relative Error Test: \", relative_error_test.item(), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3fa35b",
   "metadata": {},
   "source": [
    "### Max absolute norm error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01cf8637",
   "metadata": {},
   "outputs": [],
   "source": [
    "R_abs = torch.max(torch.abs(prediction_tensor - u_test_full))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b3e65482",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0246, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "print(R_abs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "678810f2",
   "metadata": {},
   "source": [
    "### Explained variance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "02c72385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance Score: 0.9998695252949855\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_55276/2104891356.py:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  a = torch.tensor(a)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = prediction_tensor\n",
    "b = u_test_full\n",
    "# Assuming 'a' is your predicted values (model's predictions) and 'b' is the true values (ground truth)\n",
    "# Make sure 'a' and 'b' are PyTorch tensors\n",
    "a = torch.tensor(a)\n",
    "b = torch.tensor(b)\n",
    "# Calculate the mean of 'b'\n",
    "mean_b = torch.mean(b)\n",
    "\n",
    "# Calculate the Explained Variance Score\n",
    "numerator = torch.var(b - a)  # Variance of the differences between 'b' and 'a'\n",
    "denominator = torch.var(b)    # Variance of 'b'\n",
    "evs = 1 - numerator / denominator\n",
    "\n",
    "print(\"Explained Variance Score:\", evs.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f664baf6",
   "metadata": {},
   "source": [
    "### Mean absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43fc2394",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test:  tensor(0.0035, dtype=torch.float64) %\n"
     ]
    }
   ],
   "source": [
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = torch.mean(torch.abs(prediction_tensor - u_test_full))\n",
    "\n",
    "print(\"Relative Error Test: \", relative_error_test, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e50e9e",
   "metadata": {},
   "source": [
    "### Contour plot for PINN (80 percent) and (20 percentage lem prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e3eec75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 20, 256])\n"
     ]
    }
   ],
   "source": [
    "print(prediction_tensor.shape)\n",
    "prediction_tensor = torch.squeeze(prediction_tensor)\n",
    "input_tensor = torch.squeeze(input_tensor)\n",
    "\n",
    "conc_u = torch.squeeze(input_tensor)\n",
    "concatenated_tensor = torch.cat((conc_u, prediction_tensor), dim=0)\n",
    "\n",
    "x1 = np.linspace(-1, 1, 256)\n",
    "t1 = np.linspace(0, 1, 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "58375ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 4]\n",
    "\n",
    "from matplotlib.font_manager import FontProperties\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "font_path = 'times-new-roman.ttf'\n",
    "custom_font = FontProperties(fname=font_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81507bb",
   "metadata": {},
   "source": [
    "### with coolwarm plot style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec236fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch\n",
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# # Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# # concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# # Convert concatenated_tensor to a NumPy array\n",
    "# concatenated_array = concatenated_tensor.numpy()\n",
    "\n",
    "# # Define custom color levels\n",
    "# X, T = np.meshgrid(x1, t1)\n",
    "\n",
    "# # Define custom color levels using the minimum and maximum from the NumPy array\n",
    "# c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 100)\n",
    "\n",
    "# # Plot the contour\n",
    "# plt.figure(figsize=(15, 5))\n",
    "# CS1 = plt.contourf(T, X, concatenated_array, levels=c_levels, cmap='coolwarm')\n",
    "\n",
    "# # Create a custom font with Times New Roman\n",
    "# plt.rcParams['figure.figsize'] = [10, 4]\n",
    "\n",
    "# from matplotlib.font_manager import FontProperties\n",
    "# plt.rcParams['axes.unicode_minus'] = False\n",
    "# font_path = 'times-new-roman.ttf'\n",
    "# custom_font = FontProperties(fname=font_path)\n",
    "\n",
    "# cbar1 = plt.colorbar(CS1)\n",
    "# # Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "# num_ticks = 5\n",
    "# c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "# cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# for t in cbar1.ax.get_yticklabels():\n",
    "#     t.set_fontproperties(custom_font)\n",
    "#     t.set_fontsize(20)\n",
    "\n",
    "# plt.xlabel('$t$', fontsize=20, fontproperties=custom_font)\n",
    "# plt.ylabel('$x$', fontsize=20, fontproperties=custom_font)\n",
    "# plt.title('$u(x, t)$', fontsize=20, fontproperties=custom_font)\n",
    "# plt.xticks(fontsize=20, fontproperties=custom_font)\n",
    "# plt.yticks(fontsize=20, fontproperties=custom_font)\n",
    "\n",
    "# # Add a dotted line at t = 0.8\n",
    "# plt.axvline(x=0.8, color='black', linestyle='dotted')\n",
    "\n",
    "# # Set the number of ticks for x-axis and y-axis to 5\n",
    "# num_ticks = 5\n",
    "# x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "# y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "# plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "# plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "# cbar1.locator = FixedLocator(c_ticks)\n",
    "# # \n",
    "# plt.savefig('LEM_burg_20.pdf', dpi=300)\n",
    "# plt.savefig('lem_burg_20.png', dpi=300)\n",
    "# # Show the plot\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e0864d",
   "metadata": {},
   "source": [
    "### with rainbow style"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e393a1e0",
   "metadata": {},
   "source": [
    "### Snapshot time plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "04f91104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Create the figure and axis objects with reduced width\n",
    "fig, ax = plt.subplots(figsize=(5, 5))  # You can adjust the width (7 inches) and height (5 inches) as needed\n",
    "\n",
    "# Make sure the font is Times Roman\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "# # Perform the prediction\n",
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(7, 4))\n",
    "\n",
    "# Make sure the font is Times Roman\n",
    "# plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "\n",
    "\n",
    "final_time_output = prediction_tensor[3, :]\n",
    "final_out = final_time_output.detach().numpy().reshape(-1, 1)\n",
    "final_true = u_1[:, 83].reshape(-1, 1)\n",
    "\n",
    "# Plot the data with red and blue lines, one with dotted and one with solid style\n",
    "ax.plot(x, final_out, color='red', linestyle='dotted', linewidth=12, label='Prediction')\n",
    "ax.plot(x, final_true, color='blue', linestyle='solid', linewidth=7, label='True')\n",
    "\n",
    "\n",
    "# Set the axis labels with bold font weight\n",
    "ax.set_xlabel(r\"${x}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "ax.set_ylabel(r\"${u(x, t)}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "\n",
    "# Set the title with bold font weight\n",
    "ax.set_title(r\"${t = 0.83}$\", fontsize=26, color='black', fontweight='bold')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 3\n",
    "ax.set_xticks([0, 0.5, 1])\n",
    "ax.set_yticks([-1, 0, 1])\n",
    "\n",
    "# Set tick labels fontweight to bold and increase font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# # Set the fontweight for tick labels to bold\n",
    "# for tick in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "#     tick.set_weight('bold')\n",
    "\n",
    "# Set the spines linewidth to bold\n",
    "ax.spines['top'].set_linewidth(2)\n",
    "ax.spines['right'].set_linewidth(2)\n",
    "ax.spines['bottom'].set_linewidth(2)\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "# Set the legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.savefig('burg_t_0.83_20_lem.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('burg_t_0.83_20_lem.png', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d96305e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Create the figure and axis objects with reduced width\n",
    "fig, ax = plt.subplots(figsize=(5, 5))  # You can adjust the width (7 inches) and height (5 inches) as needed\n",
    "\n",
    "# Make sure the font is Times Roman\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "# # Perform the prediction\n",
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(7, 4))\n",
    "\n",
    "# Make sure the font is Times Roman\n",
    "# plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "\n",
    "\n",
    "final_time_output = prediction_tensor[-2, :]\n",
    "final_out = final_time_output.detach().numpy().reshape(-1, 1)\n",
    "final_true = u_1[:, -2].reshape(-1, 1)\n",
    "\n",
    "# Plot the data with red and blue lines, one with dotted and one with solid style\n",
    "ax.plot(x, final_out, color='red', linestyle='dotted', linewidth=12, label='Prediction')\n",
    "ax.plot(x, final_true, color='blue', linestyle='solid', linewidth=7, label='True')\n",
    "\n",
    "\n",
    "# Set the axis labels with bold font weight\n",
    "ax.set_xlabel(r\"${x}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "ax.set_ylabel(r\"${u(x, t)}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "\n",
    "# Set the title with bold font weight\n",
    "ax.set_title(r\"${t = 0.98}$\", fontsize=26, color='black', fontweight='bold')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 3\n",
    "ax.set_xticks([0, 0.5, 1])\n",
    "ax.set_yticks([-1, 0, 1])\n",
    "\n",
    "# Set tick labels fontweight to bold and increase font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# # Set the fontweight for tick labels to bold\n",
    "# for tick in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "#     tick.set_weight('bold')\n",
    "\n",
    "# Set the spines linewidth to bold\n",
    "ax.spines['top'].set_linewidth(2)\n",
    "ax.spines['right'].set_linewidth(2)\n",
    "ax.spines['bottom'].set_linewidth(2)\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "# Set the legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.savefig('burg_t_0.98_20_lem.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('Burg_t_0.98_20_lem.png', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d962cd38",
   "metadata": {},
   "source": [
    "### Contour plot where 80 percent for PINN solution and 20 percent for lem solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a93e09d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = concatenated_tensor.numpy()\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t1)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='coolwarm')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$u(x, t)$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Add a dotted line at t = 0.8\n",
    "plt.axvline(x=0.8, color='black', linestyle='dotted', linewidth=5)\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "cbar1.ax.tick_params(labelsize=26)\n",
    "\n",
    "plt.savefig('Contour_20.pdf.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_20.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5011fef9",
   "metadata": {},
   "source": [
    "### Exact contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8d6ac2bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = u_1.T\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='coolwarm')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$u(x, t)$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "cbar1.ax.tick_params(labelsize=26)\n",
    "\n",
    "plt.savefig('Contour_Exact.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_exact.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f54a3df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RHwNgn/b+Oe7+s+iJVCHumNn+AG418K89Fp+y+yxpfp67bxh47VkAHgTgl+6+5zLdnwTgGAB3B/AhM9sVwGcBbAPgmQBe07b7oruftvTF7r7ezF4O4MMADgJwupm9EsAlAO4B4E1ohJ85AFufgUbEHdiUcO4wX04zxLFEfckRTeajXELUfIiLZ3o+6b6ihKsSnUt9HKs0EWSuxoV3RmdKac6vqP2nrw6OsO1T2OeeE23DnqKPYyphjue+ng9qROJXZirJuWNmJ6PRBADgBHdft7SNu3/fzD4L4HAAdwLwTjN7nvtiqUQzuxNubhTZqp8IqhB3APwrGoFmKdsC+NaS/+0F4Bdsx+6+0cwOA/AlAHsDeFf7N8j3ADyto4+PmNld0QhBDwNw9pImmwAc6+5fZ+c1HMPm+W5JgVkQzTPiTsbQLUZsosYKEqQANoQn34I56vOYqVFQKG0sZt/IKILUKGwxVCl+MUyxQMYgEW18qEVlhfuGEONS2n5fWijvNDPN54zVwADY7OT2fzPbC8DRS/79JwP3H9qmXxnkU+5+3gqHPA7AfdFE/BwN4AAz+xCaStn3APB8LFbK+shyppEIahF3Joq7/9zMDgTwt2jy4+yNxmlzEYCPAnjnYKjVkD7WtS6hF6H5YG8L4HIAZwF48xg7ys3HATCXEHeYZMkWJO5wC4f0YHFiU9wFbZQIUlp4V5ggRRD1lc04pHIuCKMuxkrLlRN1YVPa9mEobRtGESWQlbYgoiD2wxrFryjkKhB9o7QfOsT4ZL0OkNCWFwNmJijuoKlK9aqO5x/Q/g1yMYAVrdnd/Zdm9hgAp6DREu6NxaJMg3wUwHNWMgZDFeKOuz940q919+sAvLb9W+lYZ6ERcyaGO7B5rvtAMEK5oYQbop8wISko5Iq5UGe2DwDMh4VTxQhOpYlNOceqUZBiyOqikmiVhdK2YRR9FQIY8au0zyInM9P71mPRmlGIFaFQ1T5jsKjQjUJw9/PM7E/QuHSOAHBXADuiMXx8G8D73f2MSc6hCnFHLOJEWBZzgc0IHFFCSZSQRP0iwwg3gWFZURe+zHYsTWxiKE2QKk3gmAsZifzcg95XaS4qhhoFl9K2IUON25mhr8IWg8SvGCSSjU9fq11GoeNQVIcBNjt+kaBhtKaLsb8U3P1ZAJ41QvsbALy5/cuOxJ3acGBzIoaJ2YsZDYQ53qgjJqPjhhOSkk2avphk0dRFPzFWlJiSUWxiiHLcxIV35Vt89jVMrK+CFENfBZfSFgU1iik1bucoavy8cjOfbjLVMNeACpnpRsdhN6UJf309H4yCYeJhWVOJxJ3KcACbE6srZmHOCDdOrOIYDYARU5hzNnP4M+IGe5EVJiYxgwUtDCjnEgEXJhZzMcaZrWI+i7ko8SumG+7YyLhozHlxmPN9ZRWkGCjBOx81LgpqFFNq3M5h9PRt0Wj90sk44tc5Z39zq/8deu/7j9FjnUggKwe5zEgMvQvLKgGJO5XB5NyZYYQbooIVJ26kx+LKrhNtqLHiLp6jxKSocyn1/mOGolxLOYUkhjjXUrqNxKbxKc39xCBBanwkWnVTo2hFMe3X7xV+ZFFMeqH7V0c9cav/nXvRFSvuLzdRxzPjJO8rpSXGl4jGYnLuTACJO5VBiTtU6fEY4WaWWFFTrhwqB1CaKNEK4Ba6XFhWTNLpaRaSGCQ2pfrJKTbF5H6K+kGnr+4nhtLEAka0yiq4FOaiYihtH2MobT/MTk/XL5RIvwoL3aoW1xVNtVQU9lgnZphoKfRppbRrFiGEEEIIIYQQQggxAnLuVAaTc2eG+SWScWYwrpygnCpcyFW+ULJmvHQb5lcrypUUlJeI+eGzRpcQA6NUR/3KyMyZqjJDxb+lm+TMf8QgJ1E3pVWVKs0JUprLozi3EQFzHVAcFU6ZpqzdI45CP7NS8pmU6myqCeWv6TfG5BIRIyFxpzYcmEv4D5mS2UxC5fmMIhEnbkTlAOKusmoNJ0uRM8E1AyMkUWJKUD8MNQpJDGEhaRKbuscKW+mVJUhlDasp7Vq+sMW7BKnC6OtbK2AXOvhPt06ePK5gEpanracffM7v+tLEr9K+N6tGCZUngsSdynAAmzd3t6FydMym20SVS48SieJyAHFfJFFCUZRIRDk4qIS4+dxGDBKSugnLbVSY2JTTtcRQo9jEQO2rEpvGpjT3E0Vh06lRkGKpUbiqxTHxbx/6/Fb/65q53DQBTPHmKc15WzdKqDwJJO5URpNQufsLYYa4eibW92TVrXSbKJGI0j8CF2hUmXNGBAlanecUkqLcRgxRQhLlvGAWYOmhsjpTOCEpX9gRdczXKCQx9FRsynmh3lexiUGCVACFTYdlvrTtSCBBajqR+DU+2g85TM6diSBxpzLcgU0J5w7jXpklnDuM42Y2SNxhFoyMAMSJG+k2ACfcMOPNU9XL0v3kDEsLE4mCPvsaw9YYJCR1U5qQxFCj2JRVSGLordiUhjnvZKU0TaKwzUNT2nYkqFGQqrH6XWmUJjoUJzATSPziUc6deCTuVIY7sHlz9xedExY3SkwhlBtOlImZzzxR65ARrViROEooihKJOCdRup/SRCKGKLcRs0jjBI6MwlaUYFmYENBXIYkhp5gyH3WN2VfXEoPEpnKob53XUNhm7Ks7gzlnlFY2u8albY2Ci8KgCkTOnYkgcadC5gKyejpxdqOcO1ToFiFKEAd32AKN/B6JEookEqXGihER63QbpfuhfonMGbYmIamTuJwy6TZyLXUjsWl8JDYFUdq6ssLN2FdBSg6pPOR0JNUofq0OyrkzCSTuVIa7J3PucK6c9MHEhBMxLiEmdGueKPHFiC05EwEDgAflCpJIlBor3Q+XcDoqJI2YDyMEUL3U5zaSkJTqJ+bCL2qhm9O1lHNVGeZaIpgJykHGUdjFcGFiE1Der/TFiVJlbR6OwjZhGDV+FgRVilZB1JivajVQzp3JIHGnMhzAfErcIVw5UQmVGZj5MMptWCgZ+T2SNeeQRKLEWEw/6TZzQQt4RiSKEwKm120kIambnEKSwt+66atriaPAi/PCpsT8WJaT0sSmvrpy+ppYt6+fF0ONoX8lo5w78UjcqYwmoXL3FwsTKsXBCC6M4ybGJcT1k2xCb58Zpqx6xpxDEolSYwU5iagQsBpD0tJtynMbEYNJSOruJmd+nxqrXPXUcdNf1xJLYQvLwqYjsWl8+ipwTLMgxVDjZ1oscu5MBIk7teHA5k3dmjCXUDmmTVSsZNR5nfuO4AaLy9/DuCFyLlCJfiQSdY+V7iavSERdZKb7YcbifpEqy+UR9SvaTNgFbcxF5nzUF6eEpE5Ky6PEjdVT1xJJeYJTvgVMjYvzccSmBx2ww1b/+9qF148zHYoaBSmGKPGitNDI0vZ5AQAmcWcCSNypDHdPhmXlhKlg5USuHMaVk9MlxPYVVuY9KJxMIlHEWPlEIi5PENFNYXmL4kKKmPkEOeOm2G3EECUkRS266xSS0mRdMPbUtQTkdS4xTHNOptUQm7r6ixIdGKd4TkpbI0d9pqWJRAxKqMwjcSceiTsVMpdUVGLisqi8PIRwE4UFhZtFXjtTC7CgLy5qocuEkgWJREx4GyP+MeG2jPMizk2TbhMlOnBOmXwiETcfZqdn5lNhSFpGt1FfhaSwsDUCCUnd9FdIAkoTOKY7TC7/Z9F5zJa1a4RRWqgdA3XuqfEDq++jWBWahMrKuRONxJ3KcCIsa55wgcwSlgrGuRInJMW4chj4C1pi8UmcmYw64eZzCTH9ME6iOeJqtUqXULoJRU5nSo0iUVhy64whaTk/U4qgi/m+hq1JSOqmt0ISsApiUop873+6XUtpSsuZEuXyyFrquzD3U2nmD+azqNGRNAlUCj0eiTuV4e6Ym4tw7kR5IdL9zBNXGlHCDeMUYUqqN8SEiuXMJ8SITYzjZm6OmE9Gl1CVIlFUPovCRKKoCk1x26eskDSGMFeO3EYBlBW2xiAhKU1x+VB6GpbFVQ5aDedO9iGXhftRrqx9tbScTVRevYyfd1gOthodSdGYcu5MAok7teHAXPIKOp9ww5yUZjNa7qJCtwBOuGEUZ+4ic3pdQhKJuilPJGJGSzfKKRLFJZMuKyQtLgF2mr66jUoLW2OQkJQmrysp3SbrQq6nQtIwvvj99VvuLxxjpZSiLs21VKPYVMAudjNqDH8rGYVlxaMtKoQQQgghhBBCCFExcu5UhsMxHxCW5fPpuJsZKi9PzhAwopdAQT1KnOd+BOlnCFhcNbF0G8YywTgvwnLuEKFtcgAle0q2CHMAEbPJ6gAiqDHfUGmhZAyl5SSSS4ghY46bwnJn9DtRdoqybB6l5T+K2lWZc08UYaXZFU5VHE1CZW3PaCTuVEaTULl71ThDBJ/GiTIMQbl7mBCwwCTQUc7UqBMKE3I2S3xJpsP64kLAqNL0yRYAgoSS0kLAGKIqjuUVZdJIJEqNlW6TMyl1ThRKNj6lJbdmCBWSmPDIsPdW1uKkr4myGbE2p9ZUWojTNJffLi3F1jR/FqMicSceiTu1QSVUjqIsAYj7soybM7OAp/oJsoJEuZKYi7Go6mWMkFSaS6hGkSjufaXblCYSUUdzUBWw0kSiKEpLSi230fiU5jbiyLsgihKTcuZVKU1QqFFIijrGoj72OA06yuGSL0l2lMAadc6IgssxJgEIMOXcmQASdyrDASIsKyf5BCDuxB4XAha1+GQiighjStYwsShBigs7inEJUZ8XkwCb6CbKTRMVShYlXuQUiXKKMmGJhysUieLmHENpi4Io+uo2ihPI0uQVkoBprpTG0F8hKeg7qKxDNVC0yveZelbFJaabMFGmtMTVq4HCsiaCxJ3acGBuc0niDkNQZS7qCyBObIobj+iFOVcwIWfElThzLp1lFp/EmTKnS4gL7woKSSvMJRSV56W0fEMSiVLEXBRRiyaJRFkoTiRiqDBsDVDoWoq+Cklh+ajC3NQx/UTlgokSiWokp5umtHPP6iDnziSQuFMZ7o55ZuVUHURYFvU9mC95c+R4jMBB9cMIN4SViNnFGEGKm09MeFdpLqEaRaIa8w0xRIlEeZMuM4Olm/RVJOpr3qLyQtLSlOY2YlHoWjd9FZKmOUcSQ05hOGeoKke+9z5X2H64apSWMKkHSNypkPnkzwV9FH+AWWJpxb1z9pKOEWVixjPqFymin4x5gqKqXFEuD2I+3AUJIdwQK0tKCGB+1osai2iTM5SMW1jGiESUUBK0igurXMaMVaFIxOUZSPcTtdipMm8RQWm/+FbpNgKqdBxJSNqaVzzvYVvuL/TyhpO+NLR9X5MuT7MjKWceJWkRcaha1mSQuFMbHlMK3Yhf8esL/wJmqJMke8kfJcowJeXTvTAiSE5XTmlhYvPMpIm6pFEl56fbJZRuk7N0fZhQUlrlsnSTvCIRATfn6RWJGBSSFkONjqMahSRGdBhnE174va9v9b+oY2Rc+upIopIuZ3zrNYahigaFZcUjcacyHMBcprCs2TVppaA8ASgywTMhyhC9zAV5L6KqheUUyWsME4sqOZ922E23S4ghaygZMR/mmq7KymXpJhKJAijtErY0kSj3r+ZVOo6CFpbUfh/l4FgFoSXH+jsq110UEpLykN1h2GfM5NyZABJ3KsPdMV+QoMIIQMwiN/fvURwxS11q4U0wO0uEpRGV1KISRXOJkJk5E06ZjGFijLsnZzLpvrqEmLGqDCVjpkOQMym1RKJuJBJ1k3UBW5jbCMh89SK3EcW4oU6TdhuNSk4hiTnGcpaBrzFsTXoFj5w78UjcqZDUgtCJb5654vLy5CupzhJWDpIQOBAk2MX9opCeD5cDKSZPEKXJMGfToDAxLmyN6CcqkW1PXUJUft4KQ8lyVp4qLSk1NRbRZppFIk6Mqy+5NUOkkFSiUJRimt1GozDub2qr4Tbqpo78R6PSV7dR2Hf0FCDnTjwSd2rDgbnN4wszM1TlJWYhU5rjJm4+UeIFN1iQALSG+Zk+3YQjJidRaWFZzHzmCXcPczEWFSbWV5cQc9KvMZQsZ+n6nPmGGEoTiaIob85BwnFP3UZAeWFpOanRbTQsae5r/3Xr5MnjagZ5t0+6TU73Sk5XTmluI0Vc5UUJlSeDxJ3KcDjcu0873MIzhlnCniABKGY8p846OcOy0r2ECTdBuzTjXqkxTIxJJj3NLqGwsKMpDiXrrUhUmMVeIlE3vRaJCgsFYsiau2fIYHc/5CFb/a9rSKayUs7tXGOSbI4K3UbM+SAsx5hEDcDYCyIxAhJ3asPTYVfzhYVcMQKQU9W7ot5XZM6dNFECEJe/hplPGmo+QaXpKWsq42wiBovKE8RcrZYWJiaX0PiNolxCOfPphIWSEWR1yoSJaMRYTCOJRAnKEomA8oSiac9dlIL6/qWiYjMmAy4sf03OMudcGGp9ydg5V219x9dqwfwAJUZD4k51eLJaFuemiRFKmIMyKr/PDBFyFCcAATldQDNBbqvSBCAuLCtfouiceYKiyskzFwmMW48JF5JLKNUoo0so3SRM4IgS40rLN5RXREsTJQ7mpK8iEdBvN1GKvoaklVNupKGvuY3ivhfkJJpqTAmVJ4HEncpwwrnDiCmUm4ZQ1KPGmnoBKGPSZU4lJ0qzM4YbYqQo11JUVbaoPEFR4SdRjpu+holRYXRyCXWS0yXEEJVvKIqcoWQ5xcGclCcSAaW5iaLeG1eBKGYshaR1k9UpE/RZ5HQbKSRt2lEp9ElQ2vlfCCGEEEIIIYQQQoyAnDsV4smfEaMqYaV/8p0JcuVQoWQzhMa/Od2EcfcAzHZmf3Uoy91DuZuIMKionDtR7p4o+zP163phSaCVA2h8SgsBY373o9xGGUPAGJRMenz6WnGMIfKX/tJCxUpzAOWkr2FiUdRY3YyiwlAyhrwuoYoxKKHyBJC4UxtEWBbzFcYs9GbmiVCYIAGISag8T5UCTzdhBCAWKoqF6kkCUOd8GG9zUBn4qPAl6nMPSgLNLGKpfZX5XlAOoLEJE2WC8gTlTBicMxHyNCeTZphmkQgo8b0VJhJlDO+KosYwsZxhdFGhZFE/XjGhZEyYmKgXhWXFI3GnOtKl0MGIMkzeGWLvYAQgJxw3c4TgMruGcPdECUBAmAgkASgxHeJ9Ua4T6rNPj8UYxHJWAaOSbTPV5hi3UUbHTZQbojSXEOfgqM8lxFyAKU9QN6UJHMVVHCMobRsCJb63skQihnHe+6fev26r/x1x9Nb/GwVVLusmKjcYQ5STiLrmCPoscm6fmjEYjLn4EiMhcacyHOmFwQyzOmVEGSrUIypUKt0mqwAEUEeHEQtvrqx4mmkWgGaY1XnQnKOqblGVlTJWAWNUEG6xly9MTC6hxFg5q3fldAkRKJQsDzVWHGMpbUnRV5GI+aFjHFPkpz/w2q3+9+TnrFt5h5mZZrdRjUmp5SQKxBBnAxNbkLhTG57OBTNPnNoZAcip8tyM64KoUES5E4hwM2qhx+bcyVktTAJQ52yoNxYzZ2YRy31eMQJQjVXAJAAlxiIuIJkL46gS5tPsEmJQKNn45BXaOMoTU2Io733lE4kW6PoqyhlullcEKUskyvnea6S0kMbVQqXQ45G4Ux0ek3OHM8cnW3CHJJN3JSjpclBJdZaZjO9NAlBiNhUKQJzgElMGPmei6Kg8QQoTS4wll1Ce+aSbFOcSYphmkQgoz01U2hKnvPfF76/e8SU7zTmJGPqa3DouVIq4dumpIDUJlHMnnqrEHTM7HMAxAA4CcGsAvwFwBoA3ufvPVtDfgwF8dZTX+DJnDON/TtjF3a8cZbzl5zB+zh3qtD1DJEIOchUwp3+mwhcD28980KWNBKAEQQIQd4KImbMHJTBmKK1SGOfgiFFB5BJKNMkYJjYXVUkto0uIosKE0wyliUSlCRdAeW6i8sSUGEp7X13CzyjUkpNokJzJm/sqEjHkrMxVNWbcL1ljD2MG4KkAjgJwIIBdAPwewI8AfAzAye4ekml1BE0AAP7D3Z8VMe4gVYg77YfyXgDPWfLUXgCOBXCUmT3N3b8w4an8cML9FwX3a25Mfp/SKnwBoKp8MQIQ41BgiBK35ol+Zij3F0M+AYjaFYOExigXTJQARB1ihVUKkwCU6CdIAJoj5lNjyfnSXEIMCiUbn9KECyCvSJSz2lFprpNhm/kJzzx+y/2Ft5Vn7vkcB2Fuo5Be8lJjTiLBM2nnjpndBsApAB665Kk7tH8PBXCsmT3R3X810clkogpxB8ArsCjsfBLAiQB+C+C+AN4GYE8AnzCzQ9z9ohH6/QaAHRNtHgLgv9r7H0y0PQbAR4Y96e7X81Mb1gnhlskpuATl9ymtwhfAiUCMABTnyokKXQsSOCQAdbcIEoCoolvpJtQhn7NSmBJFd5M1ka1cQokJEe+dMg4q4fS4RFYxyrnQ5aropdsU5xIqQCR6wjPXbbm/sJ3HNfZFva+ofDGM2ygqcXWNUTKMcMMQ9Xn1NU/QRJhgzh0z2xbA5wA8oP3XrwGcBOBiAHug0Rb2A3AwgNPN7L7ufm3Q8D8E8OpEm4mIScWLO2a2K4BXtg9PA3CkLyZ0+C8zuwDABQB2QCP6PIXt293nAHQKLmb2pPbuPDqEm5YNIQLOmFChUj0VgMIqfIETgZgy78y8JQCNP1ZfBSBmKEZwySkAlZYous48QfW5hBgBiAtfKsslRI1FtJlmlxBDTpEIqNNN1NtQsgJEokHCwmqyiilB4jolWkV91xPdUOfUfGpKXIl32YTMLCy6YQjHYlHYORfAw9396oHx3wngswAeBWB/AP8A4O+Dxr7S3T8b1NdIFC/uAHgGgO3b+6/yJVfg7v5zM3sPgOMAPMnMdnX3yyIGNrPtADy5ffgVd780ot9xcHiyWlZU5vFkbh+gOAEoqsIXwIlAUVW+KOGKWoClm9QoADEnbu5irCwBiPk1jkmoHCUAUYtPIrl1TgFIeYISY2UUgJxyG5XlEuJExjRyCY1P3lAyYJpzDvVWJCLa5Mw7U1oYXV+dRFHvXcLNKjAh546ZrQHwqvahA3jGoLADAO6+3syeAeASNFrDC83sDe5+1UQmlYkaxJ3D2tufuvv3h7Q5BY24MwPgsQA+EDT24VgM2/pQUJ9FwDhcouIgw5IuZ03wDDCXCVFCCbManiNSfc2uiUmIwghbXH6jqN+PY0LkShOAGKgk2UECECMWMMmt40KuJAB1d1RamFhZLqGokvNcSE19LiHmbBlVlp4hZygZEJfjhnH91egSYqhSJKowNCms0lOFTqKc+YaizgdzQUm7p4EJ5tx5KJrEyQDwZXdfNm+uu19uZh8HcDSAWwB4AoD3T2pSOahB3DmovT27o805aFZ/s2ji5qLEnaPa2xsAfJp9kZlt6+4bg+YwMilnD8C5e6LCuywoE3reBM9AlLE9KhEys625X+CZ/EbEew/KbyQBaHxKE4BylopXpbBunFgVTHWYGEFvXULMWD0NJQPKSzrNIJGom5xJqRlKq3JVo5Oory4hgUlXy3rkwP0zEm3PQCPuAMCjIXFncpjZ7mhy6QCNZWpZ3H2Dmf0WTXKkuwWNvSuAR7QPP0Pm0nmFmb0FwE5mtglNwqYzAbzN3X8RMa8oShOASgvvajorq8oXA1sJLEVpAhATksZdHEoA6oJxXjCOmygBiCFnpbCoRNHF5QkqzCU0zWFiDDW6hCgyilZAXqfQNItEWYWJjIJdaUJJTkoL6azRJSRaJufcOWDg/vcSbc8Z8rpx+GMz+79oEjbvCOBqAP8PwJcB/Lu7/y5onK0oWtwBsPPA/SsSbS9HI+7cLmjsP8fi9mFDsvYduL8Nmg90PwDHmNnz3X1otS2jspMB299633SjIKIEIGqswvL7AHEuoKgkz1ECEOMkYtw0jAAUluCaCEmbIXLB1CgAMfmGqBAnBqrCF+ESqlAAUp6gRDeFCUA1homVlkyaoa8uIaDPSacZYoRh6iNjphNElADEUFy+oaxumph+SnPTlOYSmgaiojuWYXDB/ItE2//FYgTQXc3Mlub4XQELpdYXuH379wA0ZpCXu/vbxhxjWUoXd7YfuL8+0fam9naHzlY8CyFZvwHwpUTbbwH4DwDfRlNm7QYAdwHwVAAvB7AWwAfM7Cp3Py1oftVQZX4flJfkOS4PTpR4EXNJMhP03vsqAHEl1WMEF4qg+cwwsUCUs0kCUGc/FQpAjP+wxjAxBrmEEmNFuYSArE6h0pJOc8uWOlxCF/3grC33F0ba954PHmusqRaJgvqJorz3VZZLqGoMK1W5djazQbfNSe5+0pI2Ow3cv7KrM3ffbGbXArgNGm1keySqaSf4GYD/BvADAFcB2A7A3QEcAWBvNLl93mpmt3X348cYZ1lKF3cGP/HUUbDQduyjxcwWat4DwEc9YStx9/st8+8fAVhnZqcD+CqaD/btZnZGW4JdCCGEEEIIsULe+vKHbPW/d5+hhbMQPeZKdz800WbQ7JEyiACNSeQ27f0dsXJx50Hu/vXlnjCzVwJYB+DV7b9eY2ZfdPf/u8KxlqV0cWdww26XaLu2vb0hYNyjBu4PDaVicPezzeztaBw8ewO4N4D/GafPkigtd88E7X0rJs5NlM/d40EhaYy7h7F+M8SVrifGkrunm7BtGBW2FoPcPYmxwn7JLisELMzdw5CxUlgUNYaAAeUli2ZQniByxEK0nZx5gvobKhXTT3nvS3FZgIWl9yiFYcJO+9wcgH9o8/r+VfvvVwF4TOQcShd3Bm1UuwxtdfPnx6pNb81K8y/ah9939wvG6a/lVDTiDgAciGXEHSeP8h12+uNCTlk8pQlAkdWywhI4B+UTihKAOBEk5n2FlZMniCtdn24iAShB0Dbk3le6DZP1jNkLp1kAciaPR9jnXpYAVGUOIILSRKKoEDCgvGTRYQtUYjoMNYtE435spS03S/ssyguVSlOjADQVTG5DXI9FJ85apJ04gyaS6yYyo0XWAXgumqijh5jZdu5+U/dLeIoWd9z9UjO7Ho21aq9h7czsFgB2ax/+ZMxhHwTgTu39sVw7A1w+cH+noD57RXHJm4GsCZxLE4Bylm+nklJTCx5iPgSlCUAW9N4lAKU6Sjdh4mmZxWBcRS1CcMlZBYwRLyrMAcQIQAzKATQ+US4hoL/JonMuvEtzpiy2Hd44LFF0YVWuSvssShNTGGqcc9UYItXfpVyDRXHndugQd8xsDYBbtQ83IyYKaCju/hszuwjAH6PJv7MngB9H9V+0uNNyHprM0vfuaHMIFn9UPXfM8RZCsuYAfHTMvhYYzJZ9TVCfYhkikzeXVsErpwCUs3w7IwDNEcmSZ9cQgkvQVW+c+JVuMreZEJuC3rsEoFRHRBNVAevuh5lOYQKQzzGVhdJj5QwTY1wO3D7fT5cQ0ONk0TnnHDLSeM6UuxzwoC33tyTf7NyUhSWKLkwIyOkSqlFMyTnn/mOTdO5chEVjyJ4AftnRdg8s6gg/DaiUxTAYaXSboa1WQA3izqloxJ19zeweQ8Kkjmhv5wF8YaUDmdnagb6+FFiD/gkD988bpyMj4hMZF0yNZA3vQnkVvHIKQKWVb2dWqNwv3jGrz5yhZBKAApAAlBiL6GiKBSCG0sLEGPrsEorK5VFjPiHmu4O5vqG2DzMhgnG+6V9w4le33F/43LuO65pzCXVRYyjZNIeJ9XOlNjoTzLlzIYBHtfcPBfC1jraDyZkvnNSElnC7gfvXRHZcg7jzQTSxabcEcCKAxw8+aWZ7Anhe+/DT7n7ZGGM9AYu2LCoky8x2d/dLO55/AIAXtA8vBvCdMeZHwQkc/fxaiQzv6nOS5xTc/pEvwTMDU+KdQQJQgsIEICZ8iRKJJAAlxiI66qkAxKAwsQACXUI5XUBcOIxcQl0Efb1wYxWWv6ZGkShOPCUGI8gpEtV3RV8wBi4R38o4E8BL2/uPAvCmjraPHrh/xqQmtICZ/RGAfduHG9HtKhqZ4sUdd7/MzE5EI+wcZmYfb+//Dk2o1tuxWI/+VUtfb2Znocmj80t33zMx3EJI1nUAPktO8VQzuwzAKQC+C+A3aL6p9wLwVAAvBrAtmjCvF4xdBt3Sv7iwzhSRh2kO7ypNAGLCqZgKX1ECUF73U7pJjQIQJ7QFuYQkACXGIjoqTABiNGEmt1FpeYIY8oaJpYl0CdUYKhYlAGV13BTmEsrqvChMmMgqEhWWeDjnZ0FE6XKRRlqqAbBJxqd9FcAVaAouPdzM7u7uP9xqBma3B3Bk+3A9gM9NakIDrMNiFOnX3D00x0/x4k7L6wHcBcBzADyt/RvkegBPc/eLVjqAme2CRfvWp9z9RvKlM2gUv0d3tPkDgOe6+5krnV80cvekiQrx4i5sJAB1DxUjguQs8c4IQPObifkQ39I5BSDq+IkSv9JNJAClmkyxAMTlnYkJlyktTxBFX8PEgCJDxSLIGSbGUJpLKEy0KkyYyJnkWORBOXda486EnDvuvrk1h7y1HeqDZvZwd796y/hNOpb/QGMSAYB3uvuyVbfN7GQAz2wfnuDu65Zp83oA73b3Xw3pYxbA8ViMOAIaw0ooVYg7bWKjo83s8wCeD+BgNOFTv0Fju/oXd//ZmMP8ORa3x4dGeN1LATwCwH0A3BnAzmgyX18D4Eft/N7r7leMOb8tJA8E4uqZEyWmVwAC8ub4meb8PjkFoJwl3qMEoNLyH3EiCEFPBSBmkT+zhth/JAAlGgUtTisME6sxTxBDpEuoxFCxFKWJRDW6hGqsOMaQt+JYPgdQjcmko7Zzf1dPI2CYtMr1bgBPRpO792AA3zezf0eTJmUPAEcD2K9t+yMA/zTmeMcCeLmZ/Q+Ab6JJ6vwHNGXW90cT0bP3QPsT3b0rF9CKqELcWcDdPwPgMyO+5sFku7ejCfEadU5fBvDlUV+3ciwtBARdPUsASpM1yXPG/D7M+6pRACqtxDsjADHMFBb+Ns0CELUJCcfWNAtAc8TiIqpUfGlhYn3NE8QQ6RIqMVQs2Y1cQp1EuYSiwsRKq/RUY5hYX/MExQltfccmmXMH7r7RzJ6AJnXKQwHcEcsLOOcCeKK7/yFgWANw//ZvGDcCeLm7vzNgvK2oStwRcVAigASg6ogK74pyEpXGPLF9clb4ihKALMhrLQFo/KFyCkDcIoVIOD0blUQ8DaOvhkUdFRYmllUAYsgYJkYtrAJdQqWFijHf0dzpWy6hzrGI+dSYTJohSuAozSXEHDuUiEbMpjQBaCqYcKygu19tZg9H45o5CsBBaKJsrgbwQwAfB/ABd98cMNyj0Ig690WTMHkXNFWx5tCUPv8BGkPIye7++4DxlkXiTm0YI5bEiDLUdJgT8pQLQKWFdym/T2qo+gQgp35dJuZDkLUCGqEEzDBvrKcCECVMEDBCG3OBzbk8iCY9DRPLmiiaoK95goDyQsVyhokxv93NEyvU0gQghr6WnA9zCaWbUEgA6ian06p6MqhcbXqXT7R/K+3jWQCelWhzNoCzVzpGFBJ3KsOQPnkxC+qo1QUjFEgASlOaAMSg/D6JXjIKQNziIt+qMWr7cGFrxP4jAWh8lCg6MRbRUUYBiKG3eYIAaiXHnAu5BWGdFcWS3TBhYowjacLhJ686cuulzBv+c7iPMKuYUqFLKGeYmASgKccmG5Y1rUjcqQ5LhlQxC+HSBCCRj5z5fSQAJXrpaYl3hrgKaEHOAglA4yMBKDEW0VFYpTBiqJ7mCQLqzRWUHKswl1DOMLFRojfGPUZy5gliyCkSRZX6Zr7G54hY3qioHS58Mt0Pt31ikklPBdoQ4UjcqQ0jfm0irjJLE4CU3ydNlLsnCsq1pQTPiaGiBI58+X1KE4BAREmHJSeWADQ+EoASYxEd9bVSGAMpJpQWKkbtZ9Sv/VFhYjFjlZpMelxXRM4wsayJmTO6uzMWNxM1I+dOOBJ3KsMMmFnTnWZ0fjMhhUsAIvqqTwQqLbyrxgTPOXMSUStdIqtwlBurrwKQUc4CYiwJQN1jSQAam7zbuad5goDyQsUqLSmfHKvQZNI5Ql645MQxyaQZsrqEcs45aDp5q4mlJ804gKaCCSdUnkYk7lSHYSahclLJVYOu5hkBaC7oCzVnhS+gvy6gvgpAceFdZZ1o+lrhK6cAxIStUUgA6m4iAah7rIwCEENf8wQBcaFiVJ6OqAW8wsQSg/Hn5q6E0SXkCRokZzWxnLmEciaTFpViFndyEFuQuCOEEEIIIYSojhM+vGnLfUV4CCGmHYk7FZJSw2eJOA4icAtRP9VakJMoZ/l2dry+unuiUPWu8edTWvLmGt09ebdPuoncPYmx5O7JQmlJoDl3Aplzh2hTWiLoqPwszLlwmnMA5awCFlc2uyx3D0Nx+X2IfsLeuxxJPNoQ4UjcqQwzw8xsIucOId3kFIBmnDjZFli9a5pLuCt5czcSgJIdpdtkFIC4xY4EoDGbSABKdkQ0kQAUQo2JoGtMAs1QWg4gRgCaY0prM8fGCEmgx2WOSOJCVbnKWFGL6YjZDZnr9aj8PhJugpHdLhyJOxWS+hIzQkyhxqH6IRa51GhpKSlr8maQgoIEoE5qzN3DfaYSgMYmowDE5M1gkAA0dhMJQMmOiCZRi+7SkkAH5jvLWgksKGFwjUmgGXLmAAIhpjAOoNVIAt1FVKn4aXYJMd8vcu5kRjl3JoLEndowYDbh3GGIc/cw/h5iQU2dTDJW7wJCXUAR1CgARVGjAMTAXURJAOrCiO8pzs7PTIjoRgLQuE0kACU7SjehfskOSpzKEPVZ0GHVYYtYoh9iPgw1JoEuLUwsTvzKlwSaMS50JYheIKdLKGep+ChdOEoAynjKmA6kcoUjcacyDBaSc4chpwCUKu8OAPObk00CBaCmZbpJ3gpeKUoTgHKGd9WY3ydraFvQnHMKQFHOuNKqd0kAGruJBKBkR0QbppvCqoCFfRZAYK6giMn0N08QN1iQAMSEU1HiV7JJ1jxBYQJQ0EI6qysn6LeZqF1VrpxgFJYVjsSd2iCcO5SY4jEHEyMAMYsUZrFDLcyDyrcDmcPAJAB1UmN+n9ISPEflJMoJ90tbWfl9JAClGkkA6u4o3SRMAGIoLE9Q1C4P5M0VFJUsuq95gqg82RkFIIbS8gQxUMmkC3MARe0/3LFTVhLo/mNSwiaAxJ3KMDPMrKlvkZaEcOUwe+v8ZiJMjLw6ZBbMc0EX9NTCO6MA1FdyhncpwXNqqLLCuyQApTpKN5EAlBirOAEoqDpVYQIQwIalEQvLwpJFK09QarCyEkUzcNXE8oWJ5cwTxJAzUTS1q2ZMAt17DMq5MwEk7lRIylJamvjDLKhnmD2REIA8qOx604wJG6mvzHuNCZ5rDO9Sfp/kaMRQEoDGRQLQ2E2mWgCihupppTAg8DMLyl+jPEFb89F/eeRiP+3tX7zsix0dxZzjg1K4VFlOPsoIXFqIU2nz6TsOwLVBw5G4UxlmwGwiP80c4V4pTQCaI4QbRkiJK/EOMJc/NZZ5lwAUMVY/8/tIAEp2lG4jASjRUbqJBKDEWBkFoLjkvMR8ggQgIC68gksGzMwn3SYqKUjU50F99pT7abJ5gn590Te2+l/XNiitUhiDysmPT40OoP5jyrkzASTuVIdhZnb8A4ERgJgLn6jkzVFEJYEGOBGI+yTKKvM+zQJQaZSW30cJngOQADQ+EoC6mxQmADHkThRdmgsoZ7WwnJXCGKLyBEWtAUurFBbmEsqaTDpmLIaoSmFyABWKxJ1wJO5UhhkwGyDuRMEsQIxwt8xkLArICEAAN2/myre4Mu9TLADV6O4pTQAqLcEzt29IAEp0FIIEoLGbVCkAcd9jUcl5mRkhLgwsqsQyQV7Rrt+Joqnwow5KqxRGdVNYMmlGAHLicpwSpBi3ERVGF7M/S7jhUVhWPBJ3qsOKEneiYCp8GXEySYWsRVNnmfd8AlAUEoAS/UxxgucakxKqClg3EoDGH2qaBSAgfxhYiqzl4qlkyTEJY0pLFL1A1ymRORXGiZExTquo9W9UnqAaYY4LZjtHhW4JNBtTzp1wJO5Uhll5+XJKYn5zjOMEAGaCQs6mWQCa5hLvUSjBc6qj+vL71ChI1Qi3gE03kQCUGCvn1yp5+ZMzDCwsrKawcvG1JIp+you/MDBWM1hXKE9ppeKpoZhzfFBOGUrQJBQpTrTK5wBizPhUDqCgPGQyrLRoQ4Qjcac2zLAmlVCZUaeZEzLRhqlgXiuUm4g4W3BJnmNyIDFXddNc4j1nTpm+ogTPqaEU3jUupYloYb/iSwDqJCrUtxkw3WSaBaDSSsUzDDsf7L7PAxaHasfqOmbDItsmnCh6kNLKyTNEhYkpUXTP0XV5OBJ3KqPJuZP6QijrQOmzAMTA5PihFnvE0TpPJMqe5hLvfaW08K7SEjxHuZYkACWGqlAAYsjqVpMA1An7WWR1bQUmgk52o0TRY8PlTMlXKSwqTCwqgXFxYWJBiaIZcm5nAQCmnDsTQOJOhaTDsmIWIDmJimvvNYxKFiQAxZWUry/Bc18pTQBiiMvvU1ZZeglAyY5CKC2/T9gCJEpx6KkAFEpGAYjZP7jS7DECUGml4im3UcYcQFlLxTMEhYlF5QlilJu87komBCxiNhzMvtF7DMq5MwG0RYUQQgghhBBCCCEqRs6dyjCLqpZVn7tHOysB4e7xoJCrGSLfUGnVu+Tu6abG5M05q3epfHsCuXsSHaWbhP2yPsXuHiCwYhQ1WLpJaJn35GBEk566e0pI8Dwq3NsqLH9YRncPta8WZv5nLoFkWGlwbYhwtF6uDDNgTSIsizpxUUmXYxbLOXPuaIeOgQm54r6O0z1JABLDiBKbGHImb85ZLWue2OclACW6kQCUmFBIk9DcNVlLhjPk/FyZVXVGASiKnAIQA3fOiPm8qLc1xeFdeZM3Bwl/UZ9X1ZiqZU0ArYUrJCahcpRzh+mHKPOdcbHT552euRCdCdoAjABkQQtmCUDlUFruHlXv6iankMQlsZUANDYZhQKGMDGBGIt2S0Tl+JEA1N0kyFURtQ2z5m0qLAdQaVXASqvwFSUAMXD5ffp5jTgqcu7E0+d1bi8xM8K5QyxAiAuWuSDnTpQPhNlZI51ENR4cs04kQiY2AFMti0m6zDBPzKdGAWiamWYBiKGv1bvikmNKABqbKKEg6LjIKQABmZM8SwDqbjJhAeg7//2GLfcXPop7PeoVHfNJjxX2mRYWJpY1CXTGMvA5BSCGnCXeq0fOnXBqXL9ONVwp9CjyOXeYELDcJdWDilNlJadbhCnxbkRenpk1hLMrowDk88T7Yn5pIObTV3cPQ2kCEIOqdyV6Kaz0OMM88ZlKAEp1U5YAxDaTABQwVNg2XPkxf84X/3mr/3WJO2E5gCQAdY8VVAY+itIqfAk0O7acO+GUtjYVCbicO8yXE3NyI/ohTDnMfKLILYRHCUDMyXQzMdoaYjSmn5zMMSIR9atMTNJcDwq5kgBUF2FikxI8d/dSWH6fnKV4JQB1EyYAAcXlAcq523MdpZtQi/yoxMNBpdAXyHHezPqZZs0TFDJUXJ6gnoaAcWpTv3EALudOOBJ3qsOSzh1qTUmIMnPE8cYtLoJyRwQlimZhLmwY4uYUI9zUKAAxzG8mdmpqsRfjhoi6uFR+n25KC++a5gTP3C/i9QlAYeFdBHG5R6ZXAAIKzAMUJF5wjgliPgwVCkBbmnYMmTMHUM7PNO+5J2cuN6IR82O0RIQykXMnHIk7ldE4d7q/oOaYFDdZYYSbmDxBmwgXyBo6VwyzIdN95Q3vkgDUBSMAMbmEuENMCZ5zUKMA1NcEz1E5iaa5wlfUAqQ8oS3dJEoAAsrLAyQBqJtoR0nX/h+WV6WwSmGYi7mOpvYx6r2n2zBOaeo7kbgo48w0MSKRciXzOCS6RSNxpzIYcYf5HmTCqahy6UwIWMYwMQYmUTQLkyuIYS7rLwoxws3sfExCZQZm4U0tQohvPEYAmiFyCZWW4HmaBaAoSsvv09cEzzmdRBSFCUDRISydQ2UV2tJN2PCLSBdQCkpII/qZZgGIYdhnf9BD/37L/YWFd+ciPWwbpvthBKCoc3PWyojEe895WcuJVhkmMgIyEgGAqVrWBJC4UxlmQCr/LKUqM0I4cwJkhKSMYWLUwoF0NjHb0TbHfDuXl6CtrK+GNdgm2WYzNqU7IsxGToiI3KkovaNJAMpDzgvjKKEkb36fGGdTcRW+woS26S3xXp7QxjULcwEFua0kAHXDuSuXn8/BD33Zlvuzs/kcW6UJQAylhYAxMJ9XVH4f6QyrgDZ6OGWt4ATFbELcYU7+jFPEosSd0lxC5DkpKhE0tY02l1Vau8bY5JmMTiJGH5QAVBfF/TJaXILn6a3wVZrwV5oAFCW0hX5v5AwDkwDUPZ+MAlBUvpgaBSCGrJ9pxs+rNPr6vsIxJVSeBBJ3KqNx7nQfCJxdMqoSVn0uIXbZzSXWJUYr7IuLOaHYpuISN4WQM0sQswUtKGeKBKA81Jjfp7QEz9Nc4Str4moJQGkkAI3dUWkCUI1l4BmYPEGMABR1zqBKxQcJQHNELiFmTUPl9yFC7qM0GbryX49xhWVNBIk7ldHk3OluwyRUpkKOmAqFxImiNJcQK7ZsJtw0zMXo5s3MyTQ9H0aUYd7bZiJ6aZopLU30PDEhZlHNCKgMlOgwxQJQFErwnJpPXxMP5/vc+yoAsXDhdukmEoC6Ka1iVFTSXIacY1G7c1m/NfYW5muqRof8RNB2CEfiTmUY0jl3mC9v5iQQFU7FXCDMMiIRo/AnXE0AJ7YA3AV07lAxUQY1CkCzxLf9HJFMmvrlL0gAqpHS8vsowXM3VFWlngpADPPE516aAATEhdtJABp7OlkFIMYNS1GhA6i0ELAod09f8/uopFaDnDvxSNypjCYsq/sLgRFcohR+ph/GSRSWcyfItQQARF4+SpSZJQQn5uTOnOA4R1ZMKUwjsmBHjVUaOR0lTLUaahtSi50Yl0fU9umru0f5fbopLcHzNJd4jxMiY3KYsJSXb4lYNEoA6qS488GEq4DdbKgKQ8C4sYhGQSXeoyp8hWnZ/cx+MCKmUugTQOJOhaScO/OECMJ8L+cUiZiQEUYJZwSZOfKktDlImKDCsoJcQsy2jgrv4nIg5fvSrlEkYmBcbQxMifdZpJNSc9cjyu8zLn3N7zPNCZ7LqzxVVoLnWgUghqhwIQlAiSaMWyTofTFk/dyDQoE4p2I+ASjr92ZYEuj0UArLapBzJx6JO0IIIYQQQojq+N0v/mfL/YV14h32vP8qzUYIIVYXiTuVwYVlpfuZJzLCOxGiwThumAz11A+IRBuq6tYcp5ZTIVdU/pEYxxHnlInKE8SMlW4T9et6X105pSF3z3RSmruHIWf1ruLKt4flUaIScKTb5CzfThJ1PMvd000J7p7//tCTt/rfM/7ht8PHkrunez4VuntyRrPmdPf0HoOSjk4AiTuVYQDWJEQXQrdhQlip+FQuDCrdhjm2o0QiZs4AN+85QihivsA3EwlxmQuAWeKDnSVEOyZPUM4cQDNMMiWCKBtsaWJT1hxAhAA0Q3wJqXz7+NSYvLnG6l19Ld8el1C4PAEorNpRYcm0JQCND/eZpvuRAJSYT2ECUN7EzOk2zPmp/xg8KnmV2ILEncow86Rzh0oaxjhuGAGISIQ1w4gJQa4cTmzivuCjhCIqnxDj7kl87gCweTMjlKTnE+USmmVO3FE5gKh+kk16S2kVvhh/jwSg8SkteXMUNVbvqrF8e05xMEoAAuIqeEUd8xKAuokSC7a07Xh/ccJEeh6lCUAMfc0pE/aZFlbhq2YcgBe2n/QBiTuV0Th3ur9ZnPjVfJZJukwIN8zJnzlvMW4j5mTCCFJkJVVScMrnJtpECDdMP7PpqBrMEM6dWSJRNOMSYj5XZj7Mj+tRSaCjBCmGqNA2BglA3fRVAIqitPCuaa7eVaPgwMAeO2EuoCkWgBgoISCjALRA17zCwmGiDnkC9ro13VFMNzkrfDHuHq6iFjEfgigBaKp/bRxACZXjkbhTG0aEZREKB+O4mWf6CSo/OMcIUozNMSiUDGAreKXbrCHmtDkovIs64TLuHkYkooQkxpFEzGcTk4ciPR8qBIwqAx8TbsawMaSXOKIEIGoBHxRWIwFo/LFqDO8qTQBiyBm2xlBa+XY2RI4L8ylLAGLI+3mkmzDnuUkLQLe/4322+l/X58/l94lxP+XcN0pzADGUtg1FflQKPZ6qxB0zOxzAMQAOAnBrAL8BcAaAN7n7z1bY5zoAxxNN3+XuL0j09WAALwRwXwC3BXA5gLMAvMXdz1vJ/LYaA044d9L9MK4cJm9GlJA0w/RDzIcRiaiTCYA1xMmCKatOCVdU8mpmPskm1EXLNkQ/mwhRhkjPgk2UkBQTAhYlNnF5gvLl99lU2IUNIwDFlXhPt2EEIObYYaCECQlA3WMVJgAx9DXBM7N9ShOAgLg8SfOE+BclADGU93mkm0xaAHrokZ9YbENYk+MSPOcTgEoLAWM+d87dkzNUtawEz2FurKqxLM4da76EngrgKAAHAtgFwO8B/AjAxwCc7O6h5nUz2x7A8wE8GcBdAdwKwGUAvg3g/e5+ZuR4g1Qh7rQfynsBPGfJU3sBOBbAUWb2NHf/QvbJtZjZ8WhEosFvjzui2ZGONLNj3f1944+TDsui8h8GKaWM4BIlNlHhZkwiV/K7mxmPOTGtITYAI9ww82YWqGuIsCxGlFkTJO4w/WxiFvBRLqGgZNJhSamD2kwzjAA0S5wN54gdmhIUeioA9ZWo/D59TfBcmvMAQFiSZ0pMkQCU6CjdJEoAMuKHsrwVvuoTgBi4ROvpJjkTPIsymXTOHTO7DYBTADx0yVN3aP8eCuBYM3uiu/8qaMyD2jH3XvLUndq/p5rZRwE8293DDfpViDsAXoFFYeeTAE4E8Fs0Dpm3AdgTwCfM7BB3v2iFY/wKwN07nh+68c3s6QDWtQ+/AuCVAC4BcA8Ab0KjEv67mf3U3b++wvk1Y8Exm6gcFPblzQg3zAmZCZVi5hMkADFOGrYvJuSKGY9yCTHCFeXuSU+aEVyYCy0m3IwZa5s1TD/JJthEfOOtITpi5ryRmPMsEdvGiEQbgtxG03yBxAhAzMKBuQrvqwA0ze6eviZ4zlrhK/AX3JxVvuL26ZhFPkNxglxYhaaoZDAEzO4TM1JY/po5EN93zA+bUdcKTFESIp8O5aYOWtMQKUspB5CMO21C5QmGZZnZtgA+B+AB7b9+DeAkABcD2AONtrAfgIMBnG5m93X3a8cc884ATgewa/uv7wD4MIAr0egCzwNwOwBPR7MbHDXOeMsxkrjTiiffi55EYsxd0YglAHAagCN98Qz4X2Z2AYALAOyARvR5ygqHcne/fgXzWwvgn9uH5wN4tLtvah9/xcweAuCHAHZDI/T86Qrnt4WUc4eBES7IolIhY0UJN8x3xBryi4S5Zpn39JmJEYC45NXENiIW+VR4V5SQxFT4IvphRCJGAGKSUm/DCECEKEMJUmuIXB6MI4lJRhmUKJpzEuVbEJUGYVZDlAAUhQSgRD8V5veZ5gpfQH/LvOcUgKIEuZwCEHMRGCXElpYvhpnPNP+AUxp9vQYaCZt4WNaxWBR2zgXwcHe/enF4eyeAzwJ4FID9AfwDgL8fc8y3YlHYeT+Av/LFE/LHzOzfAHwDjYPnL83s4+5+2phj3oxRnTtfNbOnuvsZkZNI8AwA27f3X+VLvr3c/edm9h4AxwF4kpnt6u6XZZzfYWjUPwA4fkDYWZjfNWb2RjQf9qFmdqi7n7PSwRjnDgOjTlNumiDBJUzcIWCvezhRilikBYlbzDUtIzYRTTBHNJpnXFIZhSRGJNpIiDvbbpNsgm23IcbaRAhJ2xAuKkIkWk+04cLWYiqWUKXrp/giM0oAikrwzNDXEDAJQKnB6qvwBfS3zDsV3kUQdayWJgDNM7YKAibBc04BqDQHUFROmTCxP2MIWFSFr6j8PrUzKeeOma0B8KotwwDPGBR2AMDd15vZM9BE22wP4IVm9gZ3v2qFY94TwOHtw18B+BtfcoJw91+Z2bFoDCtAE/mzquLODmjcMs939w9ETqSDw9rbn7r794e0OQWNuDMD4LEAcs0NWJzfjQCG5fw5BY24s9B+5eKOAWsCxB0Gxk0SJdwwRIk7kV8kOV1JzMk953woUYYRm5hqYsSVBDOfbQmnDOMS2rxtjLiz8RbpNrcgxmLabLtt+r1TbbZJy743bkMsPonPYppzEvVVABJ54PL75AzNyRe2BuTNKZPV3RQkAEXlnWHIGZIX5V6JqvDVVwdQaTmAchK3fep775Nggs6dh6JJnAwAX3b3Hy47vvvlZvZxAEcDuAWAJ6Bx3KyEpw3cP8nd1w9pdzqa0LB90Bg/7rLSwlDLMaq4cwqAIwC818z2cPd/TL3AzB4D4J/c/ZCVTBBNZSwAOLujzTloroVn0cTNrVjcseYnupkRsmYvvK9zh73G3S81s0sB7N7Ob8UYHGuse2pR4gXj3GEIE3cynwSiXElUsuicCa6DxJ2cIhHlbCKEJGYsRkhinESbCIFj41y6zaa1hHNnY7qf9RvTR/T6Del+rr8h3c+2a9NtbroxffrZZtt0m5uI+WxgHElBVcmMEPXCyslTyV6JSoRMyGdUWFaQkBRFVHJiIfrGNIu1UcInQ2ku1tLmI/rNBHPuPHLgfiri6Aw04g4APBorF3eoMd3dzexMNOIO0ISF/esKx9yKkcQdd3+qmb0JwN8CWGdmdwRwzFLLEQCY2f8H4HUA7r/SyZnZ7mjcQkBjmRo2rw1m9ls04VF3W+FwtzezHwL4YwCzZvZ7NEmQPgjgk+6+1Q+prRC08MEMnV/Lz9GIO8vOz4zzkt79gHvEhGVNMIHVpMg+Z8aVFOQmYqDef04nVZgjqSxBinMbEQt4KrSNEHcIAWjzduk2G+YIcYeoS7/+Vuk2N21Ij3XDjen4t+tuSH/X3XD92nSbG9LFCNbfuCnZZsNN6X6YNps2psea25R+70z1rvm5mApfjBOC6YdL0FsfUa6TqAU144SIGotyzwXNJyosCSCTzAc5A6M+D25bB+1DUfnVgrYzNZ+osYK2YZWfadg2TM8n7viK2s7JJmHCn0S05pp8gs6dAwbup/IFD0bUHDC0VQetJrBf+3AzgGHRRmFjDmPkalnu/lIz+zmaMKOjAdyhLUN+E7Cl/NeJaFQooFlu/miF89t54P4VibaXoxF3brfCsbZDk0xpgduiUe8eDeAYM3uyu1+55DU7Ath2hPlhjPkBaHPuhP3uWxfOZdPLS4XfzVlFsoxiE0NpwhbVT1AbLo8S435KCzeM+2nTfLqfjXPpU9TGzYRzZ/P2RBvCacWE2hFfz5uZNkSFEEK3wRxjDyfGikoPwDjaGRhHUhR5f8WP6SdqymECELNoCtzM3CIt31hhi3NqrJChwrbhpPfpox+29RPv+/Lw74ec24ch7FgN6ycob1Fx7yumn5mg7SNtp2GCa4B9B+7/ItH2f7EYAXRXM7OlOX4J9gBwy/b+pUQE0C8H7u87tNUKWFEpdHd/p5n9GsBHATwOTaLllwJ4MYAnoVnSGYCLALwWwMdWOL/BK/JhcWsL3NTe7tDZamuuAfAOAJ8H8FMAv2n7uD+A/4Om3PoDAXzOzB64xMGTY35bMUtmY+gb5ulfu0UaI0ph5sQyhmlEvXdmzsxYxuQHCBprZj79vWFbGxS3YnY+fRzOzKXVi5m5Dek2m4g2G1NfvcDMpnQbbCDaEGNhY9q545vT29A3Ed93jCsnqE2cuhNzvJdWTSxwsJh+wkSZfPOJzLlTmroT9t6YzyPnZ8/ZM8afDDB0Ox+9zP+efvVbYsbsorBjddLbeWRyqqdUP1GfV2Hvq3JWmHJjZzMbdL6c5O4nLWmz08D9peaMm8/BfbOZXQvgNmi0ke0BjFpBmx6vZTBp807DGq2EFYk7AODun2vLfJ+Kprz319unDE2SoH8E8JHlQrZGYPATT13JLbQd6YrP3d+6zL83oEkcfRoaYeopAO6Hphb9yTnnt3UnTi2umH5qI6cIwJJzOzJCANdPUPJUSrwghAlG4GCy0wUJJTOEwMGMNTNHiCDEWLY5LRYwYxkjgmy4KdkG629Mt7nphmST+evT583Nf7g22WbDtel+NvwhPZ9NN6Tf+4br0ttw041EWNZN6c9r84a0QDa3Mb0/z20iwqkIew8VukU4gJxoUyNGVKNjYKraMVAhCEQuKgZmzlRoBdNP0PYByJCiqHlHhTgFzYch55yjE+P/+tNfHPpc1OfFkHf75AsNpcYKC3uM2ob5PgtGAFJYVsMK01tc6e6HJtoMmimIX+ZwExpxB2gic0YVd1Yy3gI7jjhWJyv+JjCzndEkV15wryx8Oq8GsJ+7f2hMYQe4+YbdLtF2IfFC+iqepHXpHIumEhYAPH1Jk7D5ubsxfyO/CSGEEEIIIYQQQkyaVf0Va2TnjpndDsDfA/gbNLFlBuD3aJIH3R7A8wB8BsCPA+Y3aGvaZWirmz+/otr0w3D3q8zsfwA8HMCBS56+DsBGNHl3sszP3DGbCOOLcnhEkdMpEknYvKOcKQwZw3yo7cM4XIhwISaUg3LBMKFJjAuG6WdT2sFhm9NhR0w/WB/juJm/Kd3P3HWE44Zw02y6Pq3Db7yOcNNcm35fjONmI5F0eRORdHnT+vS+sXk94crZQLhpNhFums2EmyaoHwYnnEQ5MaKqXRQza4LcANsEuYSI+WQdi3BURLlSgDh3U04nSJS7KcrFwJDT2bTA+j8MP3fU6Nzhxsq3nWvchqW5hBSWBTSrjYmdg6/HohNnLdJOnEGTxnUrHG+5viY13lBGEnfM7HUAXoDGrWNo3shbAPwLmkTBp6OpNvVNM3uiu39tnMm1JcSvR2N12qtjXrcAsFv78CfjjDmEhWTIOy2Z37yZXYwmEfPQ+bUsPD/m/Bxr5ojFXgBhYUAZhQuGyPCunCJIVIgTI5RwY8WIKVEhTtRYRK4TStxhMuJG5WchRBlnRJkb0202E4LLZiJ8aeN16TlvuiG9fcJEGSIMihFuKFGGEC8Y4WbuppziTlmCS06Y955TABKiFJhQzBLKpX//MQ/acj9qPjnfe2ljgbhmnycW5My3JvO+uAqLUd/RUWMR55XIHGOV4phoQuVrsCju3A4d4o6ZrQFwq/bhZqwsCuiagftM8aTBNtcMa7QSRnXu/J/2dgOAdwN43UAFqevM7H5ocvDcD8AZZvZsd//4mHM8D8ADANy7o80haDJcA8C5Y463HHdob69Z5rnvoRF3DjazNctlxzaz3dBk0R57fuaOmfnxq2WV5qaJEkDCxkJmoYTZjkw/QWIK5Uwh9kOjErUGOW4I4QaMC4bph0i+68RYvj7dD+WmYYQbQpRhBBcmN82mG9OOJEaUiRJuGFFGws34RLl7ohwu00zObRjlyokkpysnipyuHIac+YaiKC3fUNxYZTllSsszxSDnTplMUNy5CIvGij1x8+pUS9kDizrCT1dQKQtoKm7diCaqaY9hmsAAd14y1zBGFXfmALwfwGvd/dKlT7r71Wb2MDRVtJ4I4MNmdid3f+MYczwVjbizr5ndw90vWKbNEe3tPIAvjDHWVpjZLmjEKqARmpab31Fo3EyPaR8Pmx+GPD8CMQmVo5wiDDlFEgZakMoppoSNRfSTU3AJctNwbRg3TVp0CBNlCFfO/Pr0fOYIMWXzTYwok26z+ab0fKKEG8pNI+Em0U99wk1pRLlycoZclTZWzmTS7EIvKlk0NVaByaKTYxUm3NS4yI8bK+fnXpagGUVpQpLgmaC4cyGAR7X3DwXQFU00mJz5wpUM1kbz/BiN4WQNgHuiMYBMbMxhjCru7OfuF3c1cPcNZnYEgLehCeF6vZnd2d3/ZoVz/CCAdWiUsBMBPH7wSTPbE02eHwD4tLtfxnbcJoW+zt2XXa2Y2TYA3oPFZMgfWabZqQAuBbA7gBPM7Az3xZrdZnZrAC9rH57j7ucs0weNuafLDIeJMvkcJwxhIhEjOAB5RRnGdppTcAkSgMKEG6IsNCXKMGFQGwhRhhJuYsSUOUa4IQQXZiwu7wwhAFHVoGJy0/RVuMlJzvnkdJRIuMkzFkPYL/2Bok2NVb5Kq/DFUNqcaxRulCsn11hlVQHrPzZJcedMAC9t7z8KwJs62j564P4ZY455yMCYy4o71pRKe9TAv84cY8ytGEncSQk7A+0cwIvM7H8BvB7AMWgSMI+Mu19mZieiEXYOM7OPt/d/hyZU6+1YrEf/qqWvN7OzADwIwC/dfc8lT/9/AP7VzD4M4Eto8uH8AcCt0bh1/h7AwW3brwH48DLzW29mL2+fOwjA6Wb2SgCXALgHmp1pdzSup5cuff3oOGZSzp2o3CzUdPLlk+HCiYhQIfa9RwkuzHgZQ5wo4YYRXJixCFGGcdzMM44bRrhhRBlC3JljRBmiH0ZwodqsZ0KcmH6C3DQVCjeMKFNj4mEJN92UluQ4aqyw9xWU5Dj3L+I5hZsoSiuFzlDanEsTbkoLlYqiRuFGlMkEC0F/FcAVaAoaPdzM7u7uP1zayMxuD+DI9uF6AJ8bY8xPAnhle//5ZvZmd1/uF9rHANinvX+Ou/9sjDG3YuRqWaPg7m80s18D+MCYXb0ewF0APAfA09q/Qa4H8DR3X0nM2h+hEXH+vqPNaQD+clhpd3f/iJndFcBrADwMwNlLmmwCcKy7f30F87sZ5p5O+JpTTKH6CRKbcootQJwLKEyUIcKOKOGG6YdoQ/TjhEhECS6McyfIcRMlyswRggsn3DCCCzGWhJvufghXzjQLN9NMjdWpGHJXp0oRmQMnq6CQsTpVaTllyptzvrGiyCncTLOYktOVIzgmmVDZ3Te35pC3AjAAHzSzh7v71QttzGwtgP9AYxIBgHe6+7JVrc3sZADPbB+e4O7rlhnz+2b2WQCHA7gTgHea2fMG9QMzuxOavMULbNXPuExU3AEAd/+Ymf12zD4cwNFm9nkAz0fjprkVgN+gsTL9ywpVr2+iCR27H4A/QVPK/TZoEkb/Bo1I8yF3/yIxx3WtS+hFAO4L4LZoqmydBeDN7r5cvp4V4LDEwrs4wSUogS/nEgrKOQPEuWCYeVOCS5AAFCXKUKFSMWNRbhpG3AnqhxFKmH6iQpzmNqa3c07hhhJlMgo3OUWQvgo30+zKiRqrr2XFaywpziLhppvS5hw3Vn2Om7zhS6WFbpUVTiUBiGeCYVlAI6I8GU3u3oMBfN/M/h3AxWiSKB8NYL+27Y8A/FPAmMeh0QF2bfs/wMw+BOAqNBE9z8dipayPuPtpAWPejImLOwDg7mcF9fMZAJ8Z8TUP7njuCgDvav/Gpn2fZ0X01TFIuix0kOASl1A4SEiKElJyizs5RRlirKzCDdEPlb+GEC8YMYUTbph+mES/MUmFaxRu5jcR++FcvjCo0hIYS7gZn97mpims8lStwk3OylN5XR7TK9xMc6hUjaJDjcJNTpRzp2GS4o67bzSzJwA4BcBDAdwRyws45wJ4orv/IWDMX5rZY9ox90aTQma5it8fRRORFE4WcUcE4p7MhxKW4yWjcycsLClKJGLbMXMi3hsllAQJQFSIU5RwQ/RDOW42Mk6ZGMElyrmzeQMTmhTUT2HCzdzGmOTEEm7qYpqFm75WnqqxXDhQnnBTY+lohhLm/IyzFuuULKzxP/Swe624v2Y+0yvcFJfAOCM531dpYtPqYJPMuQNgSyXvhwN4Kprq1gcB2BnA1QB+CODjAD6QKFs+6pjnmdmfoHHpHAHgrgB2RBPR820A73f3cRI3dyJxpzrSOXeyumCosYJCjqLeFzMfgBNlgpw7YU6ZoLEYUYYJcWKEpDlGAApy08wRIpGEm0Q/Em6qIqfrJCd9TWDMUGLlqeRYmatOSbjppsY5D+vnu1devdX/xhUNaqwqxY1VoXBTWDhVaaFtNeMA5icbltWM06R3+UT7t9I+ngXgWSO0vwHAm9u/rEjcqQxzh6XcGVGlt6NCnHKKMlGCDMCJMlHCDdVPPjcNI8owoVJMP4wIwsyZcuUEhTgx/UQJN5TgklG4YQQOCTd56Gs4lRIYJ/rJKNyUVi489y/9NYogNc4552JYws34/YRRoXAjYplwzp2pROJObRBhWWHiDtVPkHMnaCxKJGGdO1Ti4aAcN0Q/YaIM0w/hcKFCrqJy5TD99FS42bQ+SAAKEm4YV46Em7ooLZwqaqwaExjnXFiVJtxEEjbvwhbMNc45Z0nsBbq2k4Sb8akxgXFxCbklJAE+0VLoU4vEndoIE3eCRJkgV06YKBM0Ftsua6hUkFOGCYOiEhhnFICiRJnShJvSHDcSbsohLhdMWcJNcSFOhVWeYsiZwDincBO5OK1RBKlxzjmFm1GOsa4xi1vkU2OVJXBIuEnAvC9iG04Dcu7EI3GnRlKOksJEGSa8Kyx3TZCThu2LES+Y95YzDCpKBIkSbhgxJadwEya4MKW+NxL7BlFVKioMqrSy4hJu8lCjcFNaAmOG0ipPZRWkMldwKi0EQ8JNN+Psix9+xGIhmpn2+Bn3sy2tqlRWJDqIbEw+ofI0InGnNhjnDiOCBOXTySrKhDmASOcOI7hECUBUPxJuxh6LEVyi+iGEG24+MW6aqCTHDFGunBqpMQ8ON1bO91WWcFNa5amsC8+MVaci33vOX/Il3HQz6apS991t55HmU6PjJmqsnI4bhuISGAc5booT7ArFIefOJNDeJ4QQQgghhBBCCFExcu5Uhrunw4qoxMP58teU5u5hXCnseFQ4VZC7J8pxw8xHrpxEm8JcOTnDl5QrJw/TXJ2qr2XFc7pyclanyu3KiUKunFSb1Xfl3LxNPgdHac4UbrAa99V+unKUUJlHYVnxSNypDfd0uFBUPp3CQqXixJ24nDtMcmKqn7BQqaDQLUrgkHDThRO5cuKSEyvJcQ76muQ4aqxpLiueM3/NNAs37EJYwk2qjYSbcalRuMmJQpMSMFWLpwBthXgk7tSGe1KYqdFNQ4kyGfPbAMA8M6egqlI+x4gO+cp4z2+OEXfCEhhTQltZwk1UjhsJN3lQkuM8lFZWnBFuoshZnaqvwk2fq2VJuBkfCTepflSdqnssuXJyI+dOPBJ3asM9WX67RjcNFQJFiS2MkMKJO5SbJkhwyhkqVaNws3k9s32I/YwKWZRw0zdKS3Jco3CTM8lxaSE8pZUVz115KoJaq2VJuEm1qW8bcmOVJdyUJlrl/EyLE26CKM1ptRo4TAmVJ4DEndogcu6U5qYJE3cyCikAJ3Aw7y1KJMop3FAumCjHzUaiHybEiRBuqLEIgUPCzXSi6lSJfoKEmyhKKyueU7iJIudisMRqWdxYEm7GbcMQJ3DEVJXixpJw00lpwk3Oz8Lk7gHk3JkEEncqw93TAgYRx5lTuGFClxhXTpQAxIgbwHQLN9x8YoQbSiTaFCQSEQIH48qRcFMONebBqTGBcVRZcYbSEhjnFG6iKC0sKed8WOJEGQk34zLOfN783R8ttmmFgJfc6+4dHZW1nUsbiyGncFMaYcKNnDsAVAp9EkjcqZCUyJEzDCounCooBCrI3cP2xYkpzLwl3Iw7VpSbprTqVBJuxmeaExir8lRAP4UJWxJu0ki4iehn9YUbpp+3nvOTrf73knsfkJ7YhOZT/1hl5ZQpzZUjAnGqwLMYEYk7teGerPaUMwyKcwDFhO9EhVxFhmUxwg2TCyYqYXCNwg0TcpUzDCpKTGFcOdMs3JRGcblpVHlq7LH6mlNmmoUbPiyrLOGmPKGktPnELrzHfX+lJYGuMVSKIadwU5rjRtXEAIecO5NA4k5luHtSUClNuImqFhUl3LBhWaUJN3HvP0a4oT4zJjnxhpxhUBJuSqE0Vw43VmFOmcISGJdWMpyhtATGEm7Gb8P3Nb3CTXGuk4zhOaUJN1GU5l7prXjBWE0UckWjnDvxSNypDfdkCFPO/DWcSBSVeDfIlUKINs14ZQk3XKlvQiihKkbFCEClCTc58+BMM6UJN8U5ZQpLYKyS4ZnmI+EmS5umnYSbcSlNuBnls+iaV3nbMOjc01PHDUNOV05uF2Lfcf0OGo7EndrwtHhTmnDDOHeiQrei3Db8ePmEGybEiasYFTMfpmR4X4WbaXblTLNwU1oC49JcMKXlwalRKMldVSqC0vLbABJuso1VQHLil9x7IHlyO5+u/qpcnE+x46ZGEaTGOa8OhnmFZYUjcacynHHuZA3LypdzJ2eYVDNeWcIN59zJJ9wwVaUk3IhxKC7EqbCwLIYa8+BIuBm/H4a8okS+MCl2PK4fCTfjjsUwznb+u/v/SUg/o1Ka4yaraJVzO2d03FBzDiphLgGozbmjsKxwJO7UhntSUJBwkxqLEwFqFG64kKt8iYcl3NRFTleOEhh3U1oeHAk35cyHoa/CTeSCqDThJmuel0qEm0n0w5A1pKhC4SaKvpYML207l4zCsuKRuFMZ7mnRoTThhqvglNElE+ncKUy4iSoZzrhyJNzURWnhVNxYZTllasyDI+GmnPlEMc3CTZnl0iXcjDtWcaFSEm7GHosSboLIeQyKWFQtKx6JO9WRrpYl4Wb8foA44YapGJVTuGFEmSjhJgoJN92UJtwU55QpLA+OhJuA+RS26GbIOWcJN/XtQzmFm+JCwILGoqgx8TBDhaFS1FgZw6lq3MeqxrniY2I0JO7UhnuyalTe0tv1CTeMaANwogzVhhiP6idjGFQUpZUMjyqFnlOYKA0lME70I+Fm/PlkFgJSlCY2TbNww1fLyvl5BO2LPRVuSguVyinchNHTEJ7SkjczP7RG7c+ueCTl3JkQEncqgwnLmmbhxudjnDTseFHCTV9Lhtco3JRGjXlwGKZZuBF5KE1smmbhJncS22kWbkoLueLGyifcKAwqQWH7YZjjJqebpjDXaMlI44pHV6FCCCGEEEIIIYQQFSPnTm24Jx0lNbpyuLw0TGJiok1gzp1pduXkJKcrJ2fIVWm5crixgtw0hSVLzolCrqZzPnLljN8P67rIWQ6cobxtnW+fphjDVfHNX/52oE3Tz/33/KPh/ZSWdLlCV04YOfPgRCFXTijzSqgcjsSdymDCsnIKN1FhUNR8goQbnyPFHUZwYtr0VLiJCrmScNNNaUmOi0uWXFjIlYSb6ZyPhJvx+4mcT1+Fm7z5dFZfuLlZmyH9PPHDZ271vyuPPzo9ry6UnDhisJixMlYuC/vcM1YKqx2FZcUjcac23JPiTVanTFQ+nYzCDevcYYQbpmS4hBsxjNKEm5zUmCtHws10zmeahZvynCLkd6bKgY/dpsZy4FsYsz8JN8mOYsaaZuFG7h44TAmVJ4DEncqgnDtRTpmMwg2ToT5KuGFEG4ATOCTclEONrpyosVSdKjWWhJtx++nrfCTcjE9WcYN029T43kpLTkyNFSTc5AxjKS0MiqFK4SaIrMJNFDn3sZpRKfSJIHGnOjwp3uQUbqiwJOLIndtIjEUJQHGlwBnxorTS4xJuxkfVqQL6kXCTGKufQklp85FwM/5YDDmFm9CwLKqfssSL0qoCZRUdRvhMu8asMX9NTkdJaeJg1jLwGZ1WokFhWfFI3KmNjKXQqbAkYqwo4YZxAEU5adi+oiitrHhfKS0PDjdWkOAi4Wb8fiTcFDMfCTfjj8VQmnATGb5T2mdGUaNwM+F9cbnkyZ0CTk/dNFw3ZYmDNYZK5Qy1mwZcCZXDkbhTGU5Uy4pyuFAiUZBzhwsly+ukyRlOlVdIKkskKi0XTGl5cGrMpxOFhJvpnI+EmzxjMZQm3ESGVvRVuMnqFimgYtSpz33CwHwy7vdR5BRuRB6YWCN9XnAoLGsSSNypDU+LJUwJ7yjhpq/lwvm+JNx0UWM4VdRYpQkuNbpyGCTc1DUfCTe5xgpanBYm3OR37pQVolJaGFRpiYd7GwbFIMdN91CFJbeOqtZXOwrLikfiTmW4pwWV0oSbGsuFN33FJDCOojThJifKgxPQT2HCTdRYDH0VSkqbj4SbXGNJuIkZr6wQFQk3eeircBNGzrGcuM4O+iycGMvmY/ZVptpwVndYwUjciUfiTnUQCZUpwSUo5GqKy4Wz4zHUKNzkrNDEkFO4UR6cPGPFuXLqE0pKm4+Em1xj5fvFt7T3FRty1VPhpoAwqJGp0HFTmiiTM/FwlY6bqLFyOm4k7jSGBZVCD0fiTmW4p505OUuG97VcODsewzQLN1EogXGin6kWbuoTC0qbj4SbXGPlrRiV7qcs4SY0Z0hP89fU6KYpLfFwX8OgGCTcZELCDY2cO/FI3KkNT4s3OYWbaS4XDtQp3OSktyFOEm7G7ydjHhwJN3nmU+d7r0+4Ka0SVmlJh4E44UZhUImxahRucrppGCTcjD9WxlBV6vNSPh0aiTvxSNypDk8mVM4p3PS5XHhfhZu+hlNFCS6lIeEmYD4SbsaeT53vvZ/CTXnOnfIW1DUKNzUuhovLX9NXNw3XUbpNTgGRoUbhJgjl3GlQtax4JO5Uhjswt7Fb5MgbBlVfufC+ijZAf4WbnJTmymGYbuEm43svTryQcJOjn9KEm7zOnbIW1Ox7rzJ/TWGiHTlYUDdl7Wc5nVY53TQMpe2rWUWZjKGaYZ97xTgAV86dcCTu1IanRQ4JN/1EeXC6meZwqmkWbmbWzGYbS8JNRD+FiSAVCjdxzp18C+qsThFAYVAxg+Uba4z97FbHvXmr/1379r8bazpTLdwEkdWZQlSnihKAwiphMZXC+o4rLGsSSNypDU8LGKUJN6IsOBEk5iSoPDipsWIW530VbvorXkzze5dwM34/q7+gvlmbwkKg2PFqDC2pMQyKGis4R8nY+1tfRZnSwqAYwr7rMzpuIpPD9xyFZcUjT5gQQgghhBBCCCFExci5UxnunjEsK8aVo5CrNKXlyokaS66cXGP105UzSzjISnNMyJWTp5+pduVkdEuEuXIyh2WV5nToa6gUA+XKyRnCM8WunDAKS0ot6qTJubPas+gfEndqIygsKwoJN2lKE25Kq04l4Sain/qEm/IEhekVbpi8RTkrNEm4SfSTMTlx2JxzL4RLy3GTM1SKoa/JiRc7HP5cT0WZvoZTMXDnlcI+LyVUBiBxZxJI3JlSlOS4n9RYnSonVQo3WUWifAtdzpUzvcJNlOBSo5uGYZqFm6z5W0oTbljnjnLcdI+V0U0zadfSde96+eKD0hbeNQo3hYlEWRMzl7b/TAHKuROPxJ3aoKplSbjJRU5XjqpTJfqhhJL6hJsaq0FJuEn0M8XCjcqBp8YqK+ym1sTEfQ2DqtJNk3P/mGZRhmGaS4ZXuP/0HlXLmghViTtmdjiAYwAcBODWAH4D4AwAb3L3n62wzzsBeAKAhwC4J4Dd2qd+C+B/AJzk7l9P9MHumru4+5UrmecWPC3eSLiJobRwqqixeivcZHSv5KxOJeEmok1QVbIwUaYwEaRC4aav5cBLy19TbZlvuWm6x8op3BTo7EqOJVEmYKwpFmWY+QRXiKsRB1fFPhdmtg2AZwE4EsD+AG4L4AoA5wH4MIBPuo8vR5nZngB+PsJLTnD3dWzjKsQda8547wXwnCVP7QXgWABHmdnT3P0LI/Z7BIBPAljuCNur/fsLM3svgGPcfW7kyRfKNAs3UaINoDw4yX4Ky4NTmnBTWsLgnPl9JNzk6aevwk1pi+7e5q8pzEXFLhjlpkl2FNNPYQvvaRZlqG4qLAeeVbiJGkvCDU0pzp1WcPk0GgPJILu3f38G4Llm9hR3vybv7EajCnEHwCuwKOx8EsCJaJw19wXwNgB7AviEmR3i7heN0O8OaISd/wXwHwD+G8BPAcwBuBeAEwAcDOC5AP4A4O8S/R0D4CPDnnT360eY27A+siZMFuMj4SbRT8Y8ODmFm9KEgPIEDgk34/Yj4SbRT0/z15S2WC4yBEpumm4qXHhLuEl0U5rjRgiCEsQdM9sJwOkA7tb+68cA3o9GH9gHwPMA3BHAwwF82swe6e6bg4b/KoC3J9r8ZJQOixd3zGxXAK9sH54G4MgBS9R/mdkFAC5AI9ScCOApI3T/WzSi0QeXceV83sy+DOCbaFS8F5vZW9z90o7+NkQIOOMiV04MpeXByUmNwg3lgsko3OQMXypP4MjpbKpPaJNwk6C0MCiGnMJNaWFQBYZA5XTTMOTcjqW5aRiUMDiko5h+KhT+inPcqMQ7hXsxCZWPx6KwcwaAJ7r7+oUnzexfAXwJjR7wEADPB/CuoLF/5e6fDeoLQAXiDoBnANi+vf+qpbFu7v5zM3sPgOMAPMnMdnX3y5iO3f3MxPM3mdk/orFprUGj2P3HiPOPhSiF3ldqzIHDjleaK6c04YYbS8LNuGNJuMnTj4SbRD+FhUEx5BRuSguDqjIEih1PbprusQoTSoorv811FNNPhfsPRY3CjdxPNAEpbMbCzG4P4K/bhzcAeOagsAMA7v57M3sGgB8AMAD/YGb/Vmq6lhrEncPa25+6+/eHtDkFjbgzA+CxAD4QOP6PBu7vNrSVKIJI4Waaw6lKE25mty1LTJFw042EmzxjSbgJmE9hLoc+u2mSY+V0XUBumhRR33cUShg8NqXtP9T2KW7/ySfCSgBqKCAs63AA27b3P+buly/XyN0vNLOvAHgYgF0BPAjAV7LMcERqEHcOam/P7mhzDpo8ObNocuREiju7Dty/lnmBmW3r7hsD59B7IsOp0mNxX6h9FW6qLBleoZgSV+VKwk2esZiwvpzvvT5RhqFK4abCMKgq3TQ5nTRNZ+k2hSWvpsaq0U3DMGQ+j379wCV/2+TMVx7d0U/GRfU0CzdRlCbGiVAKqJb1yIH7ZyTanoFG3AGAR0PizuiY2e5ocukAwCXD2rn7BjP7LYA9sBgzF8WTB+5/O9H2FWb2FgA7mdkmABcDOBPA29z9F10vZMup74NbMM2KImc4VZTbpumrp8JNRmGCElO2ySemlCbclJbot7fCzWzUdp5e4Waa89dUmVS4QjdNVicNIDdNisJEomHv/Rv/7xfLNR5vsNJEGYbShJucFaMKO04lJHG4F+HcOWDg/vcSbc8Z8rpxeICZfRfAXQFsB+AqABeiKfL0Hnf/w6gdFi3uANh54P4VibaXoxF3bhc1uJntgyZpEgB8191TH/q+A/e3AbBf+3eMmT3f3T8YNbdSKE24KZHShJvySoaX5YKRcBMxn5g5M9tQYVCJfioMg2IoTrgpLAyqSjdNzpAI9NhNU4kos4KO+LZjfm6liXpZxYKs+1hPt3PmENOaWc2EytYc6HdpH86hqY7VxS8H7u87tNVo7N3+LfBH7d8jALzazJ7n7p8cpcPSxZ3tB+6vH9qq4ab2dofOViRmthbAxwHcAs0H/uKO5t9Ck2j52wB+jSYh010APBXAywGsBfABM7vK3U+LmF9N5ExgHOWkieyrNOGGWeRHCTfM4jynmCLhJmI+UdtH+Ws66alwU9qCurdhUDW6aXI6adjxChNKlJuGHDKHGFKjoCDKoYB4pBJYoXNnZzMbdNGc5O4nraCfHbCohVxDlDe/auD+TisYbykXoqnC9UMA1wDYEcA9ARyJJi3MrQF8wsy2d3c65Uzp4s7gN1Xq419oG6UBngTgkPb+Onf/1rCG7n6/Zf79IwDrzOx0NDXstwPwdjM7o9Ts2kspLQ9OzhCoyL5KqzwV57jJmJenp8LNbFBImoSbkvqRcNM91vSGQclNkxgqt3NHIU4RHaXblBbiVJooQ41V2n6ocLOxkXMHAOArs+5c6e6HBgw/aAhJmUiARSMJ0AgxK+UqAAe7+3nLPWlmr0BTav3Z7b/+zczOcvefM52XLu5cP3B/u0Tbte3tDeMOamavA3BU+/Bkd/+nlfbl7meb2dvROHj2BnBvAP+zTDvq2+KutjZEvCotnKq05MV0X4UJN5SDI0i44dwi9YlEfRVu4t57fcJN1HuvUZRhqFK4kZsm1ShkrNLy0oSJEgC3SJtmUYahAOHmzFf/1WBHzW3XvCTKjE9kYvMUfRVl5LSC++qGZS2BmUnIbN39OgDLCjvt8zeZ2dFoqnQ/Ck01r5cBOJbpv3Rx58qB+7sk2i48f1VnqwRmdhyAV7QPPwvgueP013IqGnEHAA7EMuKOEEIIIYQQgueB+99l8UFO0UEIUSRmdjd0F1g6191/hdFMJABwy4H7161kbizu7ma2Do24AwCPY19btLjj7pea2fVobFN7DWtnZrdAo24BwE9WOp6ZPRPAm9uHXwZwZFAI1eUD93cK6K+T0lw5UWQPy6qwrHiNrpyoseTKyTOfKFdOVHiXQqUSbaY5x01hFaMUKpUYK2eYFMDtH3LcdFJlqFROx0RPP3cKOW4S/SgsC5hYtawjARzf8fyzAZyMRtzZjEYP2cnMZhPr/sGiTdeMOUeG76AJBdsOwB3NbDt3vynxmrLFnZbzADwATTjTMA4BsLBSOHclg5jZEwG8D4ABOBvA4e6+YSV9LcMdBu5fE9TnWNSa5DhkLEIAAcj8NcQiP0q4md22rKS5Em5SbUp77zEXvTnnI+Em0aa0UCmG0nLcTHOo1DRXgmLHY7qhvlvLWpxXKcpQ/WTeh1L0dTtTY0mU6UQ5dwAA86sYl+Xu82b2MwB/jEZH2AM3r4i1lDsP3L9oknMDtszvaiy6im6Dm+f9WZYaxJ1T0Yg7+5rZPdz9gmXaHNHezgP4wqgDmNnDAHwMzQd7AYDHuPv13a8aiScM3B8aY0dhcUJIcqgKc+VEuW0AcuGdtdR3zKIoqrJSTndPjcLNzJqcYhyTa6mw+Ui46W5TmnDT0xw3pblpqsxfk3MRF+i6UFLhAHrrzlDC4CyU9h0k4SYrjsk4d9x9HYB1ZPML0Yg7AHAousWdwSTOF448sRGx5kLnNgP/uoZ5XQ3izgfRfEC3BHAigMcPPmlmewJ4Xvvw0+5+2Sidm9m90OTWuQWAnwF4pLtfPcLrd3f3SzuefwCAF7QPL0ZjsZoofU1ynLMyFcAuhmPmxDiA+irccCKIhJvONj0Vbmw2X+hWaaIMQ3HCTY0Vo6bZTVOhKBObULmwzz5oLIq+CgE1ijIMff28GGpMPMyUOZcABPjEwrJG4UwAT27vPwrApzraPnrg/hkTm9Ei98Kia+dSd7+ReVHx4o67X2ZmJ6IRdg4zs4+393+HJlTr7QC2RxM396qlrzezswA8CMAv3X3PJc/dDY3TZwc0eXEOB3C9me2A5dno7huX/O9UM7sMwCkAvgvgN2jEyL0APBXAi9FkuZ4D8IJxc/iYWVLk6Ktww4UlMQtG7guVETgk3HQj4SbRhhIvcgouMdtHbprUWGUJE6WFQclNE0BhlaCKyzkD1OmmmepFfj5xlGKaQ5wYSnPTMNS4farGMb/66s5nAbwTzVr9z83s1e5++dJGZnZ3AA9tH/4OwNcmOanWtXPCwL/oyKTixZ2W1wO4C4DnAHha+zfI9QCe5u6jxr8dicXkSLdHE5LVxQnY2uY1g0bJe/RWrRf5A4DnuvuZI85vVckqAGUsKc6IGwCbLLm+sCwJN+P3U6NwM83lwPsq3Ey1m0YJg7spzU1TmnMFctMkKU2Qyyl+MdQokDGUJspM83ufApwwOU10fPcrzOxfARyHxuxxspk9yd3XL7Qxs9sA+BCAhR3kn4aZNQZMJQDwbHc/ecnzOwD4PwDe6u5XYhnMbC2Ad2GxUtYmAG9k31MV4o67O4CjzezzAJ4P4GAAt0LjkjkTwL+4+89WaXovBfAIAPdBk2hpZzQhXtcA+FE7v/e6+xUho1namVNjAuOcVafonDtMX8SCmZpT0MI7yt1TmnCTM4kvM1ZUxaio+Sh/TaKNhJvx+5GbJtGop6IM11G6TWkuGUCiTIpKxK9/+tjWP2i/+s8f29FPYduZoUbRobSwrL4epxXT5NxZdecO0Jg3Ho2mfPpjAJxrZu8FcCmAfdBoD3ds254F4KQxxlqDJtLo5a0Q9G0AlwC4FsCOAO6JxnwyWIzpr9394lEGqAZ3/wyAz4z4mgd3PLcOfMKlYX18GU3Z9DxYWlAJC4PajrjgDxJToqpOUeIGWy1Lwk1iPhJuxh2rtMTDM0FuI4kyCWos0V3aAl4hTgFjZRTIShMKgP4u9krb1hPezid+4vSt/vfqpz9ueD81ijLUWHLKjN+PQqWy4lx6oolPw/0aM3sMgE8DOAjAfgDetEzTLwF4irtvChh2DYCHt3/DuArAMe5+yqgdi5qwtHhTWuWpnFWnuDCpuLAsCTep+Ui4GXcsJR5OjVWfcDPVbhqFOHV3U2GIU87PPdR5IFGmm5oXzF2fSY2CC8M0izLUWD0No6ucQpw7cPdfmNm9ATwLjXPm7mgqVV2JptL1hwB80sef8LUAHgLgvmiifu6CJurntgA2DIx3JoAPu/sNow4gcacyuITKMc6dKMFldtsgAYRKqJxuw+bciSu/LeFm/H5iFlc5xSaqH2Y+CoMaez69FW7kpulGokwnVYoykQs0iTKJfipeDI87d4kyAWP19H1JuAnDAcyXoe0AAFpHznvav5X28eDE8/NoQrvOWukYKSTu1EaQcydn5anShBv2125GCJBwE9FPfcJNjRWjOCFJosy4/TDkFG6qdNP0NcSptLwzNVZnqrQUOkWft3WK6MV5DmGh5u3TOVaF7yvjZ+EK3YrDAS9J3ekJEndqw4icO4VVnoqqBBUl3FDhO/R4Em66+yHeOyUQ5vvsc4oF1L4oN003ctOMPZZCnAIoLe9MX4UCJVROU6PoMMY2fNXT/2ygn3Y+ObaBBJfxCfqcsgouqoQVSiFRWb1C4k5lGNLiTVTlqSjhhkmWHBUCFZUEuRlPwk13P/mEm9IqRvU18TCXt6gwUYahMOFGIU6JbqZZlGFg5lOaUFDaNgT6K8rUuNAd472/+qgnLD6o8b1TYxW2jxFUKbgE9SN3D8+8nDvhSNypDUuLE1GVp3IKN5SQlLF6VdOuPuEmTAQJEi9KE25KSzzMiERy0wTMZ5rLbyvEqXuo0vLOlLaIK02U6XVC5QoXw1PmEhp9LC3yq8JjSjcZ5O5hcPdiEir3CYk7tWGWFFRylgyvUbih8qUgTrjJ66bJJ9xkrRiVM/Fw1HZWGNT4/ZQm3PTUTSNRJjVYYZ8pNVZPRZkiEyoXto0YSnvv1FgVzllOmWLG6u32qZwgPU0MIHGnMgxpkSNKuMmaUDijcMMmVJZwk+onJmxtmvPXVBkGVaMow1Cam0YhTmOPJVEmgNIW1KEJlQvbRnLBJMYqcB9K0FtBYZoFF7mxQpmXcyccSYtCCCGEEEIIIYQQFSPnTm1Y+lfx0lw5YWFZGXPgsHPKmSsnZznwnFWliktOnDMvj1w53d1McTUoOW4CkOMmoJ/C3BKRv5rLcZOH0vYhgiodJYWN1dttKEJRzp14JO5UhplhdtvuL7Go/DV9FW7YhMoSbhJtMoogXM6dsnLcMNtHosz4lBYqVVr+mt6KMgyFCWQUNW5DhhJFidLeGzVWhXOuUZSR4DI+NQqIDDnF3J7jrmpZk0DiToWkFg9R+Wv6KtzwCZVj3n+UcFNlVanCkhMrx01qrArdNDXmr+lrUmGJMok2ElzGps85d6ixCtuHCCYtKHz9/B8vPmjHeuCB+4/XaWFOEIkyAfRon+8TMu7EI3GnNsySoktO4SZKTMgp3LC/rFPvfzYooXTUtg6qKpU1OXGFYVBhrpxpFmUYpjnEqa+ig0SZ8SlNlKgxLAmYahdMn8SCR7/09Vv978avfnSiYwIFLuALE6RqPC5EflzOnXAk7lSGWXrhnVO4YcSU0oQbOiwro3ATVVUqZ/4aZqywMKiceXnkpkkNRowlUWZsShMdJMqMT19FmdLeFz1eYfsHQZWizCqIDsUILxJciqGYfWKB0vaNVcDdVS1rAkjcqQ3CuRPlpilNuIkqz82INpHjlSbc5MxfU1wYVJRIJFFmbHKGOBWXd0aiTKJNYYvu0sSLvr6v3IsvuWCKGSt8G5aycC5NUOirEMmQdX8uZP+rADl34pG4Uxlm6cVnlJjSV+GGde4Ul3i4tPlEhUEFhZLJTZMaqzA3jUSZxFiFvXeGaRZlagxN6uvnTqLku930V7QqTHRgkAumKsznV3sK1SBxJx6JO7VBhGVVKdwEOSrCqleR4zHzjspfM81hUIwAJFEmYj75wo7CQpxKK5stUSbPWHLBJPqpL/wi+4KxwrAjCS5b84Dlkid39TfNQklhjpKsDpfCBCm5ewA4IG0nHok71WFJQaVG4SZrmW9W3IkqT97TMKgqkwoHuZaKE2UYChO/JMoEIFFmfEp7XxW6YKp0wASO19vFeWGL4WH79BnvfG34UMU5UyoUAiReiBQOOXcmgcSdyjBLizc1Cjc5q0VRDpjA8ZjFcM6QK7lpkoMRY9XnpqkyGfA0izJ9FS/6+r4KE2WqFFwyLwbr3EaFiQ41iogMhQkTxQklhe2H2j614nAlVA5H4k5tmIWEZZUm3GRNOpw5LCtnGFRpbhpuPhJluqfTU1GGoa8uoaixqH4y5ospbbHcV8GFQSFHaUrbz6KQ4JKF4gQFhtL2VYIqtzMjWFT4WYTjwLycO+FI3KkMM8Pstt0fGxeaky9/DVXCvLTcNUDeMKicSYWjBJecbhpmzoWJMgxZQ5wkynSTU+CQCybRT09FmQpdMNUu3ivMORRFtZ9ZANOcw4WhOKGktG1Y2vaZAuTciUfiTm0Qzp2oilE5q0XlzF2T27lTWoluuWlS8yks74xEmW4kyiTaSHAZm8JcMFW+d2qswPfVV/cKwzSXfC7tsyAobhsyVLidRXko585kkLhTGWZp8SZMuAnKXxOWuyZjCBQ9pwrdNFyuHIkyY89Hokw3fU3iW9qc+yrKSHDJNFZZ+w+LBJdMFLadJZRkosLtXNx3ggBc4s4kkLhTG1TOnYzCTVQC46hKWEFOGrav3rppwqpKSZTpHquw98VQmsBRY2hSYQvmvgouDMW9d2qssvYfhmrz6QQhwSUThb330vZDhuJEkMK2oaOw7VM1jnmFZYUjcacyzJDMuVNa/po4V06+MCm+ryl20zCfWW/Fi76+L4UmjT0Wg0SZTop7X9RYZe0bDL11G6FAB0fGbT3N7700ISCK4gQXhgo/Cwk3+ZFzJx6JO0IIIYQQQojquNW9HrfV/6797umrMBMhhFh9JO5UhyUdJWHJiXOW+Q7Kb8M5YDI7d3paDao4V44cN4k2Pc07U5hjojjXidw04yM3zdhkd5MU5nRQaFJdyClTDn110xTnsFsFHKqWNQkk7tQGUy0rSLiJynGTs6IUJUqwCZWVv2b8+fRVlCktpKi0+USNxUC89+KEidIEF4YKP/coqtzOBH0WHIpbOPV0H4oi+hgbu78at2GFIkhxx2kUfX1f0Tgwr7CscCTuVIaZYWabbbrbRIkyhblpcos7YflrZonDTKLM+P1IlEm0KawSFoFEmQQSXMZHYsrYFLlAK815Udg2Ks6ZEr19xuxPQkkmCptzccfFFKCcO/FI3KkNA2YSCZUpoSSrKydGcMkZ3hQ5nkSZVJsKy2aXNh+5YMamvPclwSUHElwCKHFBpEVjN6Vtn2AxJYc4U6WYwlDh+yru+BIkrrCsCSBxpzYsnXMnypUT5aYJqwSVM7wJccKVRJmAfkrLF9NXF4wEl0Q/ZQkuNYYL9VV06Ov7oihwMVjcYq+wbdQnZ8rV535567aTnkwkhe0bpVHcsSzCcAd8fn61p9E7JO5UhpnBUmFZOd00OfPp5Mw5A8S5gCTKjD8WQ9R8+uqCkeDSjQSXPEhwCRirrMVgjft8bkoTU6p0ndQ4Z4JpFi+q3A8ZpvgzHRXl3IlH4k5t5HTuRFVwyplzJjAsS6JMprEYCsu5U5oLRoJLN8VtH4Kwi16JKQFjlbUAmWYxpTSRhKW4RWxp8wlCQokYyhTvGyWjsKx4JO7UhgG2TffHRrlgMua4Ccs5MxPjEqIvaiTKjD+WXDCd5BWJJLh00VdhokaRqLSFp8SUcqh2AVvhvGsUSqrdPyKo8PNi6OtnWtp366rgroTKE0DiTm1EhWVlrE5FXdRkDMuiL7KiRBlGlGIoTZQpzQUjwWVseiu41CimMBT2eUUhMaUciltYlTYfEgklmahwO5dGlZ97Rkr7jq4Zh6plTQKJO7VhBlvTLe6ElQOvUZSJDMsqzQXDECTKTHXYkQSXTkoTXKoUU6ixcoaA9fR9FXYRXtyiqbT5ENQokgAFfvYMlW7rFFV+FoVR2ndraWgf45l3JVSORuJOZTQJlccvhZ5VlAkLp8qcwLcwF0xpYUcSXBKUJqYwlCa49FV06Ov7ynjBX9zFc2nzIShNKCnuM42ksG0dRV8/M4kXeejr/iNIXM6dSSBxpzosLZYEJTCuUpSJXHSX5oIpLexIgsv4SEzJQmnupygkppSDhJIACtuGkVT5eRBIBOmmr597lfT4+6VWHMq5Mwkk7tSGGZDIuZNVlGGEpFodMKWFHRUmuJS2YC5N4ChtPhJTupFQUg4SSgIobBsyVLmdSfoqgpTwmR3+rGO3+t9nT373KsxkGSo8DkvDsfr7mJgcqpYVj8Sd2jADkjl3CnPcVOqAkeCSGqss8aK0+UhM6aa3YkoBi51BJJQkKGz7MBS3DQn6Km6w1PiZhTHhY+x/zjkv+5iRSLzIQ2nnQgHAgfl55dyJRuJObTDiDiWmxAhAOQWXrEl+gSpz5eQUOCSmBBDlWpJQkoWcF4fFLQYrvDAubhsS9FUEqfGzCKXC4ycn0QJH3wQTCRPjM/XfQYWisKx4JO5Uh6VDoaIEh5n07lFcUt0Cw7KoE4rcK51ITMlEjQIZNVZhF3WFXagXt30IahRBatzOYRS2z+emb2JDNNHnA4khk2Wqv8tEGA6Hq1pWOBJ3KsPN4EnnTmE5XjI6gEJPOFPsXumrmCKhJGKs+qp3RVHjBa1EkEwUtq/mRMJFGokN4zPK90KV3yEVUeN5RRSIqmVNBIk7QgghhBBCiOr41H+ctNpTEEKIYpC4Uxtm8NmInDuFuWlyhiVF/oJWWnlyZqwaQ4rkghmfvr4vghp/ZSxtG1L01J0wzc4UOU5iqPJ4roT73evQLfcXvuvlBRClo++EBjl34pG4Ux0GX5P42DIKLlnDknKHHBUmpkyzUCIRJA+liSClbR+Kni6G+ypwSLzopspjsEBK+27tK9pfxTjofJAbx7xy7oRTlbhjZocDOAbAQQBuDeA3AM4A8CZ3/9mYfe8I4G8BHAFgLwBzAC4C8FEA73L3TUQfDwbwQgD3BXBbAJcDOAvAW9x9mVqNK5po0rkT5l6ZclcKtx0llHTSU6GktAv14i5oK7xAknjRP4o7LgqjtO+xPqN9cTqZ5u9fIVJ4ITl3zGxbAPcAcAiAQ9vbewBYWHA/291PntDYu6DRNg5Hoz+sBfBbAF8DcJK7f3vUPqsQd8zMALwXwHOWPLUXgGMBHGVmT3P3L6yw/70AfAnA3kue+tP27y/N7BHufnVHH8cDOB642dXSHQEcBeBIMzvW3d+3kvktGYgQd4LCqXrqOIk82RaXUJmgryJIcRfPhV3U1She9PXCuLh9tTAkOoyP9rGy6Ot3mZhO9B0tovD5Ipw73wJwcO5BzezhAD4C4PZLntq7/XuWmb3Z3f9ulH6rEHcAvAKLws4nAZyIRtW6L4C3AdgTwCfM7BB3v2iUjlu17lQ0G/EmAC8D8Bk0at0zAbwGjYL3CQCPHNLH0wGsax9+BcArAVyCRvV7E4ADAfy7mf3U3b8+yvyW4jDMzW47ThcNGYWSWp0iUfOWwFEOpQkcNV7wF7ePZUQXtN1M874RRY3fCSKNvjtELeh7XGSjEOcOgNklj38HYAOAO09qQDM7BMBnAWzf/uuLAD4F4HoA9wJwdPvcS81svbu/mu27eHHHzHZFI5YAwGkAjnT3hT3hv8zsAgAXANgBjejzlBGHeB6Au7f3n+nu/znw3AlmdhOAfwbwCDN7nLuftmR+a9vnAeB8AI8eCOH6ipk9BMAPAeyGRuj50xHndzPcDPOphMoMhYX49FoAyenKkXjRSV8vWvq6cOjr55WT0o7Baaavx6lIo+8yUQL6DhJl4fAycu58BcDnAHwPwPfc/VIzW4cmIiecNiLpJCwKO+vc/YSBJh8xs/cD+DqAWwF4hZl9wt0vYPovXtwB8AwsvvlXDQg7AAB3/7mZvQfAcQCeZGa7uvtlI/R/bHt73hJhZ4G3APg7ALu0bU9b8vxhAPZo7x+/NDePu19jZm8E8FYAh5rZoe5+zgjzW4Jhfmb8j63KcKLMwkVpi6IaLw5rvJCocTtHUdo+XyM17vPTzDQf7yIf+l4Q04a+W0UKBzBfgHPH3V+SecjHYzEM7GwAr13awN2/b2avAPAuADNoIokoA0sN4s5h7e1P3f37Q9qcgkbcmQHwWAAfYDo2s70B7D/Qx1a4+yYz+xyA5wJ4mJnd0t1vXGZ+NwIYlvPnFDTizkL7lYs7ZpifGd+509tyzwQlXmSVto2imGaxoMT9rDb6elyIbnTsiL6h77LJ8Za3v2Or//3ti164CjMRQoyEF5NzJzdPG7j/jqXGlQFOBvB6NO6dPzOz7d39hlTnNYg7B7W3Z3e0OQdNdatZNEoYJe6gyaWzQFf/Z6MRd9YC2A+NbWtpH+e6++blXtzauy4FsDvGTNjkAOZKcu4E0WcRYJoXKqXtZ31lmvcx0Y2OQSFWH31HT463veNdW/3vuBe9aBVmIoQYDS8l505uBnP4njmskbvfaGbfAPA4NBrEgzDcSLKFosUdM9sdTS4doElQvCzuvsHMfosmPOpuIwyx78D9of0D+PnA/buhFXfMbAbAPsTrF/rYfcT5bYXDeinu5EQXWfmY5v1MjI+OVSFEH9C5MC/a3kLUQSE5d7JhZncAcLv24S/d/crES85BI+4AwAGoXdwBsPPA/SsSbS9HI+7cLtFuJf1fPnB/sP8dASyUrmLmt/T1WzAzSrq8xwF3x1xEWJYWTSIDusASQggx7eiaKy/a3kJUQDnVsnIyaCz5BdH+l0NeO5TSxZ3tB+6vT7S9qb3dobPVyvq/aeD+YP+Tnt9WOAxzVvrHJoQQQgghRFkwaQBsaAoMIUQUDp/GnDs7DdxPuXYA4Kohrx1K6SrB4Ddw6pt2oe0o38istD9sHpOe31ZceOGFV95ln30WVLxDljz9vaXthRCh6JgTIi865oTIS/XH3F322SfdSIhyGHbM3Tn3RHJywx8uOvObn3/wzumWW7HWzAaLE53k7idFzWvCDJo8UsYQ4OYGkx2ZAUoXd64fuL9dou3a9jaZRXpI/2uXPF6u76X9T3p+W+HuuyzcXxrK5e6HjtO3EKIbHXNC5EXHnBB50TEnRF6m9Zhz90ev9hxWmYlYBEsXdwbtSrsMbXXz56/qbNXd/zBxZ3Dswf6vA7ARTd6dsebn7goQFkIIIYQQQgghVoCZ3Q3dBYzOdfdf5ZrPEkYxhixtcx0zQNHiTltC/Ho0Fqa9hrUzs1sA2K19+JMRhrho4P5euHlVLCx5boEt/bv7vJldDGD/rvkt6WOU+QkhhBBCCCGEECLNkQCO73j+2QBOzjOVrbhm4D5TBGqwzTXDGg1SQymb89rbe3e0OQTAbHv/3BH6Howj7up/4bn1AH48pI+DzZbPdGxmu6Gp5DXq/IQQQgghhBBCCFE3g8aSPYn2g3mXLhraaoAaxJ1T29t9zeweQ9oc0d7Og6j/voC7XwLgR0v6uBmtYPP49uGX3f3GIfPbHsBjEvMbbC+EEEIIIYQQQogA3H2du1vH38mrOLffYTFFy55mlkooPZh/6UJmjBrEnQ8CWBBUTlz6pJntCeB57cNPu/tlI/b/7vb2YDN78jLPHwdg1yVtBzkVwKXt/RPMbJsl87s1gJe1D89x98Hs3kIIIYQQQgghhOg/Zw7cf+SwRmZ2SwAPaB/eBOBrTOfFizutWLMg6hxmZh83s3uY2S5m9mcAvoLGNXM9gFctfb2ZnWVmbma/GDLESQB+2N7/kJn9tZntZmZ3NrPXAHhD+9wX3f20Zea3HsDL24cHATjdzO5lZjub2UMAfBXA7gDmALx0xLcvhBBCCCGEEEKI+vnEwP0XmdmwokrPAnCr9v5p7k5V3C5e3Gl5PYD3t/efBuAHAC5H45rZC42w8zR3p2LRBnH3jQAOA3AJmozU70LjxPkFgBPQ5PL5XjvusD4+0rZ1AA8DcDaAK9AITwcB2ATg+e7+9VHnJ4QQQgghhBBCiHIZMJW4mT1rSLNTsZiD994A/mGZfv4EwOvah/MAXsvOoehqWQu4uwM42sw+D+D5AA5Go2T9Bo216V/c/Wdj9P9zMzsQwN+iyY+zNxqnzUUAPgrgne6+KdHHOjM7C8CLANwXwG3RCFBnAXizu583/NVCCCGEEEIIIYTIhZkdBGBpapYHDtx/kpnts+T597n7sCrbnbi7m9nzAHwdwC3RpHW5P4BTANwA4F4AnosmMgkAXu/uF7D9W6ObCCGEEEIIIYQQQkwHrcPmAyO+7CHuftYyfZ0F4EHtw2d3JW82s4ejMZHsMqSJA3gzgL/3EQSbKpw7QgghhBBCCCGEELXj7l8ys7sDOBbAE9BEDq1FE5n0dQAnufu3Ru1Xzh0hhBBCCCGEEEKIiqklobIQQgghhBBCCCGEWAaJO5kws8PN7Awzu8zM1pvZJWb2r2Z2l4C+dzSz15jZD8zsOjO7xsy+Y2bHmdk2ZB8PNrNPmdlv2vn9ysw+2CaZEqI6JnHMmdmdzOyFZvZpM/uZmd3U/l1iZh82swcSfTj5t/NK5ynEajChY24deby8k+hL5znRK6KPufYYYc9RbmbL2v91nhN9whr2M7Nnmtm7zOy7Zrah3YfXB46j9ZwYG4VlTRgzMwDvBfCcIU0Wyrh/YYX97wXgS2ji9JbjewAe4e5Xd/RxPIDjAdgyT28CcKy7v28l8xMiN5M65szsCACfxPLHySDvBXCMu88N6Yf90t3F3a8cYYpCrAqTPM+Z2To056cU73L3F3T0o/Oc6A0TPM89GMBXR3jJD939gGX60XlO9AYz2xPAsMpIG9x9bcAYWs+JEOTcmTyvwOLJ95MA7gng9mgSJ/0CwA4APmFm+47asZltC+BUNF8ENwF4IYA9AOwFYB2AeQCHAPhERx9Pb9sagK8AuE87v4cBOB/ANgD+nXEkCFEIkzrmdkBznPwvgBPRZMPfDcCuAA4DcG7b7rkA/pno7xgAOw770wWvqIiJnecG+BU6jhcALxn2Qp3nRA+Z1DH3DXQfZzsCePxA+w8m+tN5TvSNSwF8Bs2xEoLWcyIUd9ffhP7QLPquR1PK7PNonVIDz+818Px/rqD/F7SvdQBPWeb5lw08/7hlnl8L4Nft8+cB2GbJ8zuh+RJzAN9d7e2pP/2l/iZ5zAF4FIBnA5gd8vx2aAQeR/MLye5D2i0ck89a7e2lP/2N+5fhPLeufe0vVjg/nef016u/SR9zxPgfaPue03lOf9Pwh0aMfAKAOwz8b+HctD6gf63n9Bf2J+fOZHkGgO3b+6/y9ghbwN1/DuA97cMnmdmuI/Z/bHt7nrv/5zLPvwXAFUvaDnIYGmUYAI53901L5ncNgDe2Dw81s0NHnJ8QuZnYMefuZ7r7B3xIuJW73wTgH9uHawA8fKSZC1Enkz7PjYvOc6JvrNoxZ2bbAXhy+/Ar7n5pVN9ClIq7X+fun3P3301oCK3nRBgSdybLYe3tT939+0PanNLezgB4LNuxme0NYP8lfdyM9uD+XPvwYWZ2yyHzuxHAsLjswb4PG9JGiFKY2DFH8qOB+7sF9y1Eiaz2MZdC5znRN1bzmDscjYsBAD4U2K8QU4nWcyIaiTuTZSEz+dkdbc5BY20FgINH6PuQgftd/S88txbAfkP6ONfdNy/34vZXmYVfZkaZnxCrwSSPOYbBX0ivZV7QxloLUStZjzkzmzGzNSO8ROc50TdW8zx3VHt7A4BPsy/SeU6IoWg9J0KRuDMhzGx3NAntAOCSYe3cfQOA37YP7zbCEINJ8ob2j5tnd9/Sv5nNANiHeP1gH6PMT4isZDjmGJ48cP/bibavMLOrAWwws41m9iMze0tblUGI4sl8zN3ezH4IYCOATWZ2lZmdbmZ/bmazQ+an85zoFat5nmvDux7RPvyMu19PvEznOSG60XpOhCJxZ3LsPHD/iqGtGi5vb283gf4vH7g/2P+OABZ+SZnE/ITIzaSPuU7MbB8Az28fftfdv5d4yb5oktwBTRWD/QAcB+DHZvaMqHkJMUFyHnPbobGuLwg5twXwaAAfBfAVM9t5mdfoPCf6xmqe5/4cTT45gA/J0nlOiG60nhOhSNyZHNsP3F+faHtTe7tDZ6uV9X/TwP3B/ic9PyFys2r7tJmtBfBxALdAY4V/cUfzb6EpD3sgmhPsWgB3B3ACmnmvBfABM3tcxNyEmCA5jrlrALwDTbW6vdEcHzujqVzyrbbNAwF8bhkHj85zom+s5j69EJL1GwBfSrTVeU4IDq3nRCijxK2L0bCB+z601c3bptoN638l85j0/ITIzWru0ydhMeZ5nbt/a1hDd7/fMv/+EYB1ZnY6gK+icSm83czOGFadS4gCmPgx5+5vXebfGwD8l5mdBuBjAJ4C4H5oFp8n55yfEJlZlX3azPbDYp6Oj7r7fFd7neeEoNF6ToQi587kGIxF3i7Rdm17e8MK+187tNXNnxvsf9LzEyI3q7JPm9nrsPiL5snu/k8r7cvdzwbw9vbh3gDuPeb0hJgkq3oeaReEx6KpEAIAT1/SROc50TdWa58+auD+B8fpSOc5IW6G1nMiFIk7k+PKgfu7JNouPH/VBPoffG6w/+vQJKZMvX7w+VHmJ0RuJn3MbYWZHQfgFe3DzwJ47jj9tZw6cP/AgP6EmBTZj7mluPtVAP6nfXjgkqd1nhN9YzXOcwbgL9qH33f3C8bpr0XnOSEatJ4ToUjcmRBtybkFNXWvYe3M7BYAdmsf/mSEIS4auD+0/yXPbem/tdReTLx+8PlR5idEVjIcc0v7eSaAN7cPvwzgyCBr+WDSvJ0C+hNiIuQ+5jpYOGZ2GvynznOib6zSMfcgAHdq74/l2hlA5zkhGrSeE6FI3Jks57W3XZbTQ7BY/ePcEfoerMTT1f/Cc+sB/HhIHweb2bL5l8xsNwB7rGB+QqwGkzzmtmBmTwTwPjTxy2cDOLwtPRvBHQbuXxPUpxCTIssxl2DhmLlmmed0nhN9I/cxtxCSNYemOl0EOs8J0aD1nAhF4s5kWbCd7mtm9xjS5oj2dh7AF9iO3f0SNMnpBvu4Ge0B/vj24Zfd/cYlTRbmtz2AxyTmN9heiFKZ2DG3gJk9DE0S11kAFwB4jLtf3/2qkXjCwP3zhrYSogwmfsx1YWa7oEmmDCx/vOg8J/pGtmOurQS50NeX3P13K+1rCTrPCQGt50Q8EncmywexmOjxxKVPmtmeAJ7XPvy0u182Yv/vbm8PNrMnL/P8cQB2XdJ2kFMBXNreP8HMtlkyv1sDeFn78Bx3P2fE+QmRm4kec2Z2LzS5dW4B4GcAHunuV4/w+t0Tzz8AwAvahxcD+M4o8xNiFZjYMWdmO7fhJcOe3wbAe7CYJPIjyzTTeU70jUlfWw7yBAC3Ghg3ic5zQoyM1nMiDnfX3wT/ALwSTck5B/BxAPdAk9DqzwBc0v7/OgD7LvPas9rnfzGk720BXNi2uRHAX6OJsb4zgNcA2Nw+998d8/uLgfl9CcC9AOwM4CFobHve9vPA1d6W+tMf8zepYw7A3dAkvnMAlwE4AMAOHX/bLtPHuQBOB3A0gD9pj7XbATgUwBvRlHheOOYetdrbUn/6Y/4meMwdDuA37bHxSDR5P27d3h6Jxoq+MO5ZAGaGzE/nOf316m9Sx9wybT/ftr0WwC3Juek8p7/e/QHYH8B9Bv7e2+7HG5b8/z4AbrHktZ3HHLSe01/g36pPoO9/aHJyvG/ggFv6dx2Axw55bfIEjCY51s86+j8HwG0Sc1yHxrq73Os3Ajh6tbej/vTH/k3qmGuPk2F9Lve3bpk+zidedw2AI1Z7O+pPf+zfBI+5w8lj7fMAdkrMUec5/fXmb9LXlm27XQBsatt+YIS56Tynv979DRw3zN+eQ177i47+tZ7TX8ifwrImjDccDeBJAM4EcAUalffnAP4NwIHuvuJ4aHf/OZoyksejyf9xA5pfWM4B8BIA9/VE2Ii7rwPwUACfAfA7NF8A/wvgwwDu7e7vW+n8hMjNpI+5MXkpgH8G8DUAv0BT9WRTO8evofk19q7ufsoqzU+IkZngMfdNNOEbH0Xzq+blaI6X69FUGPkQmtDIP3P3axJzXAed50RPyHSe+3MAC8lZPzTC63SeE2JEtJ4TUZg3Sp8QQgghhBBCCCGEqBA5d4QQQgghhBBCCCEqRuKOEEIIIYQQQgghRMVI3BFCCCGEEEIIIYSoGIk7QgghhBBCCCGEEBUjcUcIIYQQQgghhBCiYiTuCCGEEEIIIYQQQlSMxB0hhBBCCCGEEEKIipG4I4QQQgghhBBCCFExEneEEEIIIYQQQgghKkbijhBCCCGEEEIIIUTFSNwRQgghRJGY2VPNzM1sg5lts9rzEUIIIYQoFYk7QgghhCiVQ9rbC91906rORAghhBCiYCTuCCGEEKJUFsSd81Z1FkIIIYQQhSNxRwghhBClcnB7e/5qTkIIIYQQonTM3Vd7DkIIIYQQAAAzuzWAaxLNLnf3XTNMRwghhBCiCuTcEUIIIURJHEC0uWDisxBCCCGEqAg5d4QQQghRDGY2C2A7AC8E8DoAvwWw75Jmm9x9Q+65CSGEEEKUyprVnoAQQgghxALuPgfgejPbr/3X+e5+/WrOSQghhBCidBSWJYQQQogSuWd7q0pZQgghhBAJJO4IIYQQoijMbFsAW5w7qzgVIYQQQogqkLgjhBBCiNLYH8A27f3zV3EeQgghhBBVIHFHCCGEEKVxYHt7PYCLV3EeQgghhBBVIHFHCCGEEKVxYHv7A1dZTyGEEEKIJBJ3hBBCCFEaC8mUz1/NSQghhBBC1ILEHSGEEEKUxgHt7Q9WdRZCCCGEEJUgcUcIIYQQxWBmBmDn9uG1qzkXIYQQQohakLgjhBBCiGJoc+xc1j48xsz2N7MdzWwHM1u7mnMTQgghhCgViTtCCCGEKI33tbcPBPBDNA6e6wD826rNSAghhBCiYNas9gSEEEIIIZbwGgC/B/BMAHcFsODYOX+1JiSEEEIIUTKmCqNCCCGEEEIIIYQQ9aKwLCGEEEIIIYQQQoiKkbgjhBBCCCGEEEIIUTESd4QQQgghhBBCCCEqRuKOEEIIIYQQQgghRMVI3BFCCCGEEEIIIYSoGIk7QgghhBBCCCGEEBUjcUcIIYQQQgghhBCiYiTuCCGEEEIIIYQQQlSMxB0hhBBCCCGEEEKIipG4I4QQQgghhBBCCFExEneEEEIIIYQQQgghKkbijhBCCCGEEEIIIUTF/P/pp7sJRM3cYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = concatenated_tensor.numpy()\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t1)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='coolwarm')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$u(x, t)$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=4, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "\n",
    "# Increase the tick size of the color bar\n",
    "cbar1.ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "\n",
    "# Add a dotted line at t = 0.8\n",
    "plt.axvline(x=0.8, color='black', linestyle='dotted', linewidth=5)\n",
    "\n",
    "plt.savefig('Contour_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_20.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c034dcf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = concatenated_tensor.numpy()\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t1)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='coolwarm')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$u(x, t)$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=3, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "\n",
    "# Increase the tick size and width of the color bar\n",
    "cbar1.ax.tick_params(axis='both', which='major', labelsize=30, width=3,  length=10)\n",
    "\n",
    "# Add a dotted line at t = 0.8\n",
    "plt.axvline(x=0.8, color='black', linestyle='dotted', linewidth=5)\n",
    "\n",
    "plt.savefig('Contour_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_20.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8111f176",
   "metadata": {},
   "outputs": [],
   "source": [
    "##useless code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90799336",
   "metadata": {},
   "outputs": [],
   "source": [
    "2+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fcc46edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 4]\n",
    "\n",
    "from matplotlib.font_manager import FontProperties\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "font_path = 'times-new-roman.ttf'\n",
    "custom_font = FontProperties(fname=font_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "115b3678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sgJvO3H+Txz6s+yRCyAmBz0TsJPLnUnMSPZbFEk2UDISZcoISYsdniaSaem2dQsRL87UZ4wS073VY6uK+kyVa4GMV7b8whT6OTUo4Oa0lVsmCs8kaThrXJrH6iPnZa2KsfNZYRgkm2kpiSTlnos0oknWWFKGqrxGRewHfDnwJbomSdwO/A/ygqkZZu6XGhEsGVIWrHRkunRMAz8/X4SyB8eN9JkIhJ6NjX0L797W/bWwR1GR0weLW7sVEiM77Yo01gZ8jkTofq0Imjr6T/tATqakT+NKEU0op0vWYZkv7LexxhzKEVB9Dr+/SMwJ8KUlubUVi9VHCPxZMiq2DtYqx2JhoM4olYUmRavgX81j1S6PdnwPfVP0kxYRLBlThpqvD6yUMnYx0TVC8FoI96Y/pNVkcaRI0cfJcLLjvfsU4WWtPEsYfg4v2KUVQzdSJqGY6UW9KoikTl6Um3iEiKeh1lnmyFmuy3jd5TpURUoJwyi066td+lEy5VGzk9b0EMd4rW5FYfeSSW1sXWV2UILfmYGJsHZgYO1LSlxRtAhMuGVCEK2f7L872BH5IoFxh+Au092Rv4LMwVPB00StHBkQP7I/X+ySzI+TsCXs7o2XgxKx5X1OdzJwhk07cTyTXomRh/bYft6sZTsDHMol8JwUlLM42NFmP8hrNNEFaYqI99b0Wi1zSqRSJ4fP+KVpGtVn4+SzleZzKoutPrel1FInUcusYJVYfa5dbczAxdsQkzHDZCiZcMqAKN189DfpgPjwh6X9xn/YIjqGsjCHBczZF7jTpED1j933KBLZ9H8ZED0RKk69CJJl0a9jJ9Ilo0JWfptB8zEImigdScYFJyfhzMj4Gn/fpUrJoaqlZ32NdgigaY7eXWZZwvEcwSW5/3pUiMZYUTqXKiTnvxVKex8kkfv5Lfc6nUOLaU8cosfqIdV5jEsuPYxZcxSDAJZNtY5hwycBOhZuvuBen9LxGfT9EuuTK2S78Q2j4S7xj3YXzfg/3+ZTbdF7pZy/TpWMUoR+sLdGTQvI0qcfsI3rA//6EnMyk/JI+P2n1PKFoP94pRdCcBXnHXq/J/zsY9LrrHsvUk44lRFHMhf7q52INomiIE9E8k+QFnu+ck1ufz9RS5URq6VS6dEj5ni71OZ9EgtdJ6a+NOZQoqHwwieVHyPmZSaxEiFiGiwcmXHKgcLUuKRpILemauB9MYs961luYKXJgKFNGudrTbx9+X3r7MQ/7398fuo7KWBlQc/+sL+ldfx9tYp9kamTZ08T3BOB8PYpEE4jOk0MvsdJ9n3OIoCZ9j1PI67ssKdQmfoZKalGU8moQp6JFrFsTg67nM+vkNuHjWsKkdM73UunSIcV7ooTnLIRcIrn018YsZr6u1vYamsNa5ZQvJrESYmu4jGLCJQM74Oarhy/OgwnWGYjHBLrzS7pD5JycaOcHTu9EtEOqxBA5MCxzakKkTvgXhQyOo94/Z3HcM8YflzPifcmdiPYKuCbu9RL3i6cpB8cmw3Pv79iXZt9Ja5YT+l6ZMv4YpBJBUx7/9mM39X2RSgzFnqikXBQ7lSxKfcnQrsd4y+KoTRGT0o1LidgTviKeswBivZ9Kek5DKDFzcW2voVlMeP2t9bUWg60LKm9WelnopTHhkgFV4eYr7ipFPm/YdqZLWxJ0S5TDOKKeZUpDY2qJnHps7TGMioaWGIglcmBIonQs1OopdaZ/sMrgeOo2MSZ46jFGifzf9frxHBM9cxbXHey/47EdmtDG/oLskz9jJ46xJ6pBJz0H8mRCtlPk84y5z0v9eMYSJSnEUIrJRPO5S33J0NiiKLUgqvFakHcD4mhq9lhRE8qY302FTgSXmKQV9ZwGMOd9WOrzHUqJ0mmItb7WojDyet3Ka9KIgwmXDOgObrr5wjC0J+TtSWR7MnvWISemiJuuviFA4JyEZFSM0BA5zXGeTShHqMfUJ3FCYrUJkTk1PlInxknYmGA+EaKd0J6LFk+5Ee0/d54lddD3uksre2q6JqcpT7Tb79mQk7aoAm7qCca5MJmZ/RTxIY75fJ1IGokTO2aqk/2u5zW1IKqJKYqWkkQ1ud7HOYlVdljsRHDjJS65/utf7PMdSOj7uPTXQyhrE05DbOU1OYoIXHOaexTFY8IlA6rC2dWLN+IZMiwHfMqKRqQNHE5OT07grJWxEiRuGvGGRMRh9sthm2bpVGqJ0xxT6Mnd2dnIczXjy2KKzGkyJHZEdPBKVL7UYxw6KTh/fiNMAA4X3vXPXokxARl63Ltep8Ovy0jSyePzoD3JXOokuPlen3LiVFRKvUq0yV8sERT7eVzqymHRrtyR+HU89nwvJYlqYsiipSURzLzikomjQYqfxE18/tYiDkorIyn+9RCI14UOVvJaMYwmJlwyoMBNV/Zn7SetSfzepMpjYrcbyaX3lzaNjJKeY8bETcgXUqi8OR/DyBor7bVvwkpeRjvvf2yYPkkblhgzRc5Y6qNn/DGpA8OXGPeh+fwHLSg79z+HXQsp98Scc1WkKePYG1PrOfB77UYYl+eCzDX1ZDHHCWr9+TB3gh7l+Yx0cngiikaejJYqgqD7PZ56cegY8Zf4D23I98HSkgjmi6Ickqgm1vNn4iiM1YiDjWeglCaUuljNa+VYEOwqRR6YcMmA7uDmm0/2SkDak9T25KYtEK7uiREGszlcB8P/eZYT2O0GJvyeky1faXM+rGpcTeFUy5tJ4uY8iDTGM87ZwDoqXQsXRxU4FScDa+zMOXfzETlTV2/fe3xnLBDbpE/qNF8PMaVOTdfj1OsxE8idmvakb+x9MPfEPrRUD+JcyjqEUNlTc1Ul6wnkqcS7/PPs5zniiX/7NRNbBMF8GZTqec99GfkYfSyVtj91sp5DEtVMlUU5JRGke05NHM1jdXLA8/lem0hqsgaptC7EFs31wIRLBlSFsysn+5chPm1/ALSzSC72t1/Xc2SNa08UYQMxpQ3MFTdu+0xxA3Dms/DtPioDmSCemUNNpp5MnZxor504ke6rVvlw/nz3jPlkT45EkDnQeSIw9SSqS+p0vU7miJ2h10zQVX/mLCTodTWki/i+75WlJE/N2ZnMmFDMePwmip6aGGV8czm/THvEE/+SFrfsey2lkEAwTwSlPNEv6TLyc/tZeh2HGJPxnJIIpomi3JIIlnuuTRzFZXUiqcnQPyELeE+sDstw8cKESwZU4aabZV8OXB0WLM3J+LCogUNR0SzXOWzdjNe+dHSXHDjIrmkEGZyg1FknA8IGYksbOL/Es0+pUiRxM9Rf51M2Qp/AGbtseJ/EmfOf9zGJA9Mly97rYEmZA1GFDhxKnaHXz9SJ+ZgIbJ5kek3IEsudmrMJmSezJvlTn8fd5C4rZrzH5vbssbD1EhyUv0Y+UV+D/KlJJYFgugha4r+9oVcPW/o1O6W/HIt7xp5s55ZEECaKShBETXK8BkwcpWXVIikXgmW4eGDCJQOicPmmE3bNMprWLLw5kbzaOPlui4CmqDm8fHTHFY7qMfTO+ttyoi7N6W5dx+u6PHSfDLhKh9TwETbgsk1GhA30S5uQiczhZFaCju87ud7twsVN34R8irw5kcMsp5oxgQPDi8VO+bLaX6+oY3974dwJfRy8PjruQ+caLhP66pUgES7H3KYpdcYmTqmkTk3wpZkXFChTBA9MP7mdteaS6KB09ie/7IH8wgf6J0alyJ9U/1X1eR2mlEA1U2RQiSKoSY7XdWifua74kmoyXYIgauIji0qTRFDGlYBMHG0My3AZxYRLDlQ4vSI0L6J1qfFi3XVM9C/2XWwdkjR6olxt7FtK1OzHC5M1bps7Zmji3ydsvCYKnsIGUkmbjudihL4TYpkwMeuTN+fj9LhC1F68qnmXwPGRNzB+tZ/ZEgcObEMsudL5umkvKt3zhRxV5sCewIh5EhAidSC92AF3shY8WZpwgjd5AexJR1XHZpI85/1nkj0xRA/0lAYWcHI/NMlJ9V/Vki4xG/r6LFEELbX2wxwR1CTX696339wT/yUmy6VJIhgXRSVKoprcr5k2WxFHsxCxDBcPTLhk4GQHlxvCY3dC6wy9UT7UliHVrZ7CpSrGoaC5iLFrl8Z0CJN64qdVnFrUdIqBq93lOXsLAHfImn5RczHW/Zgy+v49I1zWgJMDB/fNV9gApx2zqb5LRXdNXkInFt3SZr9UzIeTDpEG08UNdE+u58obmCdwYPxKPlEkDiwqcmBf5gwuvBtb5tScZ7HEP+m5ehaWhTJF7ISuxTR5/ZcF5U79mM058Zty7NzXwMF7MorogdJkT01u6eMzUUmZTr9m+VOzhASCMBG09AKgsWQQ5H9P+PRfwgR/6SyKEiVRTZ8sKlkSHQUmXEYx4ZIBUbjmI+7FeXbKXqaLtk78LtXipCEspggayChpYLKogXFZc3iyHC5r3DbxmtQvKWxgvrSpJ5m7jn5DxU19v/vEjesngbyBqAInRN6cd+9xlZ7QCUvv89hcxDpShszQawwuZI7XQruBfXuLDp1+WXUfzgJTT5aROu61OlmYZJA7MP8/eyVInvOxZJI9sUQPeJQUFvCf2JDJaynyZ4lFNOe8rpeSQDU+MijHVWBiiqAmJbxvxsZQghSCvOU1JUuizSNYSZEHJlxyoMKlK+7FeenKxebdKVxIj9YHV9W+KWjacgYGBA10Spq2oIH9LBqIJ2nATdSa476KR4nN1UOpMiRqXD9joga6ZM2p57d212Pffgz62HX0MXRJ6Danopx1TBCmZNl0xent90QHpA1METdw+PztTeoCxnco0C44mATPkDcQR+BAfIkzvGh1o13E7JgxkQOFyBwg9f9gpOe9OUao2JkideDidbyU3Jl7At6euC0teWJMIAZLiaKJHgj7/I07MfKdYJcweQ2dnC6xiKbv67J0+VOztASC4xNBTUp4X8HwOEqRQmDrrkTHSoq8MOGSgROFa26S1nos+5kPp60Mkppa0AzKGYArwtlpHYvOWBAiaFx/XYKm7kMb6TSXrkqPoGmOe39rn6SBaaLmpEMOHFxSO7j8qYrd0fcSsubkpPsEcOjKQW1ChU1Ne3IQKm2ak8S2uGmXk/myhLiBEXkDQQKn72VSS5yp8gb8BA5ElDhwseB1ZJniI3LAyRzfE6iUMgfSCx2YLnUgTOzMlToQob49JBMhstyBOPX5pUkeiC16YA2ypyb35HTOxLMU+bPUpXOjX5WpQBGUQwLVLCGDanK/75p0jaUkIbRKLMNlFBMuOVDOF83V6gz90pV96XHBoZiBKXKmjtXzodISNF1SAJygaY9ziqA578db0rh4cyTN3lgHaIuazktpd0ywu5+/VuyTw8nS6QngeQLc9bzIif8aLF3C5vwEOEDYwKFomSpsumIN9t8jbrpKy3w4vAR4v7hxfZYvbyCOwIGLx2N0Ehd70nXmf8KdQuQAwavfhowjWOZUj0XqSYP0vL99WULqwOFrf+1yp2ZpyRNrUus7Yckle2KLHpg2WS5h4jl3crnUpXPHXsdLiR9IeOWljK+HPhmUUwLBsiKopoT35WoRLMPFAxMuGRCFyzfvv7l3pzTKhlplLtWtnjQlSlw5A7X0uYgF/dkze/F74sGIoIGDCc3ZWNbKaevvkQlRV/nOkKQBuNq+hLBv9klCUQPzZM1FNsr+9lBZA/mETU1zvLGlTTtmNTKvmF1ZT2cjV/jxf+z7SvQu6J28RhQ4c+VNzVnP1cLaRJc4wEnAZdlTiZyTCVkqKWUOLC90YLrUAX+xM0fqQPd7Yim5E2OSNzZ5inWVDe+ymMiTuXyiB3LKHpg+MS5hchkjo2Ap8QNlyR9IWxaTUwJBtwjKLYEgjwhaBSKW4eKBCZcMyM5dFrrJ6ZXGmilX+jM9zoXIFekUB+2sGXfMNDnj4h1midS0BY1P9szFGOMImtAsGgiXNGOCBtz9aZ/QeWXTnOiBMOm9jHY78+K0e02Vrj46RQ3MljUQIg2qLiMJGzemhtAImPD2SZt2zMFxtLOnBrNtIKW4cf3PP/k/Y2SCGkneQFyBA3hnoySROKJoQDZMSpED00SG75jmyBxYVujAPKkDy4idvvdKFBmysAhZSvCExMsleWrSyB4Y+05JJXpg/gQ4t/SJXUKypPyB/tf90uIHllkTJbcEgnJFkLEOTLhkQBSu/ci+8Nid0jqz6xEqoWIGvOVMf6ZFt5yBw+wZPzkzIiNagmbspKGd9XIQO3EWjevzUNL4TMDa2TQnJ91XE2qzlKiBwwnLFFnj4jTGsJdh4hPn4vfmic3ea2OCsHHjmidtOsuGJkobF5feuI6wx6tN/XpIKW7AQ96At8Dx+c9SbIFzGnC5dN+T7ZAJ0HnGSQKRE1RW1RpPyTIHljkx73rvrDVbpyaaCFlQgPhOdpaWPCkmoFMEQTrRA0PfQylFD8SZ5OYWPpBm3ZClxQ90vx9yiB9YfkHcEkRQdgS45nS02bFjwiUDonDp5v2ynK40dydULv4+FxaBYgbgZOCMbixrBvYncENy5lA8NMbkKWegLTmG13Zpn+guLWi6YvpJGvYmnL6SpiubxkfUnD8uM0QNBAghypM1MCBswGvmE0vYQE5pA6nFjRvHgvIGvASOb1qwz/s+mAB5kkLiQHg2To2PNJkjcqA8mQPdJ/BLZ+nULJGtM1fqwPh7bMtyB+ILHp+YpUgeSC16YO2yp2ar0gfyiB84bvljlI0Jlww0hcvJubiQ/ayUBuflPldAO0THHDED+3LAN2umXV5S08yacbEHJmOecgaGJ35tuTFWyhNd0MDeROrsRDkdmfjp6eG4p0oaGP8veJ+kgWmiZmx9rL0SmOYVsHz7Yv952usvkqxx45kubDpfNxOEjRtjJS8mTFh3u8PLm7fjjo6p7z2/gLhp9jPcJux572/keRIUMfsG3Gsv9gl2SBZOjc+J8BSJA2HZODVLiByYswjw+HExZQ7kEzowX+rA+MfgElKnZim5E3NyNWVSv7TkSTWZnL2gbybZk1r0QJryldzSZ4mrA5UkfzaJiGW4eGDCJQOiF+ur7F0d6Mq+rKi5RDOjpFvM7MmKWWJmP1aTKWLGN2vGxU8vZ1w/7dj9bd3lui/61hMdzBaCcEED0yTN7kQ58Ti5mCNpoL3eSRUzIMtlCVGz1x/xZI2LVY0ngqyB8oQNDEubZvzBcQ0cv9vNlzawnLhxfY2fCJ6eKN5r8EbMvqlfiykEDoRPWlJKHChb5ECZMgfKFDqwHqkDGcQOLCp3oBzBMxa3VMkDS4ge2JrsgfzCB+yS0MYymHDJwQ5OGiffzQl8X6lO8wSla8K/L2Vg17Ni9N7aKD0zhebY9uTJBDHj4nW3a8fvy5qBeHIG9gWNEygDX/Ch2TMTBM3udF+c9PYxIYsGwiXNef8zJA1MEzV7Jw4BogYaJS7tRZdD+uTivXbQZ0RZ48ZVjrABd7+nChtIL20cY/vnZ9uAO7n0eX6iZd2QT95AeoED5UkcKF/kwNy1YYaPTSFzIN86Ok1iSB0oT+xAAtmxoOSYM3FfWvKkLAmJOcnPJXuWED2QfmHaEqTPqhHsstAemHDJgCsp6t7XFCW7njVYpkoZaJYw+UmZvmwZ18c8MQP7MsBbzJzqntQ4iLmXxaODpTox5cyuIwNlbGK0pKABJmXRALMkDczLpnHbh4/PIWoO+iWurHHxumWNG9/If0F77vuOATEyQ9jUsVOyjLSBpcUNZJQ3sEqBA/ElzpQJROcaLwWJHNiWzKnJnaVTs4TYySF1apaSOynkxtyJesqSkM61RhKLhehXZlpE9ED7+3gp0QN2FSI/xISLByZcMtBcw6XN3pV8+hamXVTK0Hsy7pMt4/rpETOwd5YRTcw0Yw6c+OrJ4WPmK2e6xhoiZ+qxhgiai0t3+wua+nXgJWjgXNKECBoXvxEiVNLA+f2eImng4ov/YL2VhKIG4mXVgHtvdvabQda4mN3bUwqbuVk2PsSRNrC0uAH/E9xxGecvbs5jRhQ4U+QNlCdwYDmJA9NEDozLkdgiB8qVObAuoQPrkjpQiNiBReUOlC14+uKvTfLAkqIHcsqe1SBMe9MfGSZcMiAqAxkuzd/LlTLgny1z2ojRJ2Z2J/HFzG5AehyImZGsmbacGS5popXp0d2uFjS1OAjOnoFRQdOWM66f3kMmCxpYn6SBaaKm8wu3T5b0EFPUQHxZ42LGz65xO0cGNCJtxk7g5mbZnJyo1wRnrI3PBObkxO8S7aWKG9fveuQNpBU4sD6JA2lEzhxxOjYJnH+1pv7jl5A5UJbQgXhSB/o/wmNKHZg+x0smNhYUGrEyL5aWPEtcuSflmizLyp4VYRkuo5hwyUFPhsvu9GIyvDvtzlzYb9M9SfePM0PKNCbzbRnRZEq2jA5kmjTFTOdVnc7qeMNrnPiKmYtxN8faL2fCs2YIkjOuD4/xEid7BvwEjeuvel7GPndbWTQwLGn2Lm9eHdtcsHmupIEM2TTnB8URNZP7Z0DWVOMLjediJpI1ULywgeWlDfiVwI2JGx9pA/sn+0tn3cAMeQODn4Nz5M15+MRZOFCOxIH42TiQT+RA2TIHyhE6kFfqQPdHfSlSp8bkzj5LXLlna5KneATo+ae7cYEJlwzUa7i0hUEMkVK3u/jdL0tmTMr0jbU+vgtfKdPsf0zK+GTLuPENZMuAt5hprmlynoWRKGvGS85AQxp0t2vLmdExMy17xvVVtqBx/VRhpkoagLPDq20tkU3j2s8XNeBkTZeoGRvDJFEDSWQNlC9s5CTOf8HKlTaQU9z4tC1B3sB6BA5Mf80OiZzYEgfWK3IgjnDouw9Lypya0rJ0YBmpA+WJHVhO7qQUGCnWTFla8iwheMpCLMPFAxMuGRB1E+BuydGSDw3GhMzFvsNY/W3my51YUqZ9zH4/2oqHl5jxzZbpGr8LVsf0FzMu9kg5Uys2MJg1A/vPB7C4nGmOvUtihAiapoxYQtBAuKRpX/Y8hqSB4QlDW9IA1dV/OvqbIEmWFDVD40glawB2I2urDE0sRy8pf6LjE+AIwgbmS5v6cVpK2jT7zCluXP9x5M1UcXMeu7DsG1hO4MB6JA6UKXLgeGUOlCl0ataYrQOFix1YVO7AegVPUViGixcmXHKg3VcpasuLUCEDMcuNhuP4xuq6X13jrWN0fescrkUSP1smppiB/sl9iJg57yswawbSyBkIX3em5kDQeMgZ198CggZmZ9G4/sIkTee4Eksat68/VkxRAzA0h5k0DmB0PZNMssbFH9y9iLCBuFk24D+5GGoXKm1gO+LGjWG98gbKEDgQX+LMWQTTa/HdGSIH+iXIUiIHypc5sD2hA/GlDhx+vZQqdWqSy4ujzkyJiO+X7BFjwiUD7ctCn2cNnO3/3sZXyLRjNYmb2TIea7/PfsHj2s2TMjCxhAm8s2WgX16Mri8DQWKm2ZdXOVNX/JGsGRezjjF+/86ZIGdcXyPj5/A+N/vrIpagcX3LZEED80ud6jH0dhtJ0sAyouZ04nooU0XN0Fgc6WSNeCyAOye7ph6D1wR34DXvK2xgG9IG4oibkPPJrolWjqwbiCRvYLUCB9Jk4UA+iQPbEDkQTyp03Z+cMgfKXEenzVqlDsQVO3CEmSmxELEMFw9MuGSgLimqcXLkQpS0KU3I7Lfzy5Jxx4wLnqH71ozRX25VZ1AcxgiVMvtxGV3w17eMaeh+XAStY6cVM3t9BJY0Nfs96CehnHFjuPg9RNC0x7CYoAHYHQoaGH9u9/s7FDT1OHq77Rlf390MzaZx9LzeJmaxTBU1cqqDE6jJ4/HJGEksa2CesDkXHwsKG5gvbZqP3TqkDZQibobaT5ECfY9dTIETU96cd7WSLBzIK3FgOyIH1iFzYF5mRelZOpBG6sD+11QqqQPxxc7mEGwNFw9MuOSgkeHSFAl9k+8YQqZLWMwVMs147b5iZcl0xWuO47Bdv8jwkTJwuNhvf38dUgaiZcu42GnFTLOPgzEEZs3AMnLGjeFw8j85e6bRb5suQXPYb/84lxQ0sC5JE73sifJEDSwja2BcCMTIroH1CRvIJ23afcN6xc3QMTGybs77yCxvoDyBA+VJHMgvcuZKnJqcMgcO71spMgfWIXQgndSB5cSOsW1MuGTAZbi4330kids3TcgcZqQM99XsZ4qQae8by5IJF0nTs27qtmMSozmuuVKmHk/7foRky8DyYqbuM0jMVP00y1umlDTVfXf2taCcqcfhK2e6xrKooIEoWTTVns6tMSUN9J+E92fSwNAkdGlRAwyWP8E2ZI1rMz+7BpYRNpAuywbCTu772s6RNlCOuHFjKS/rBibKG1g0+wbyCByY/77oEjlrkjg1beGRU+TA8cgcWEfZVZOUYme1iMDlkRNxw4RLLpqTroOr7zS2t/9usj857DlBOGtNZAeEzFi5kttXppBpj3F+zDhSBupJ9/5YD/sNz5YBJ2Z0RFTs9uRGGjHj+vEsZ+roB/zlzKGU6DwsuZyBeYKmayxzBY0j4IQgUhZNtadz61KSBvpFzcmIuJhTaqQjlwbuYyyrZmhcPqLGHZ9e1rh+RrqJlF0DAZPTnhdRDmEDZUobiCNuQtdLjCVuho6JLW9gHdk3kE/gwPolDmxX5MB6ZA7EETqwniwdY9uYcMmA7ODSTRd/7xrPQphk8WuXulxpv52/kGmPc39MF/GGxtQVc3z8sWLGlTLgV8J0EbsOEpYtA35iplfKQLcw6RjHkJip++libtaMi51WzrixRM6eafR/sLnrvdLRLqmgAc56xEp2SXOqSN9rY9K6NNNFjY8QySFqYH2yZmgsvhP93MIGtiFtusYAodIGUosb2Ki8gc7vhzlrTJQucCC+xJkrcGCaDNiyyIH42R/N+1mqzIFlsnSKRoBrLMNlDBMumdiflLUmbFcvft8NPEPDkmV+dkx7bL79+QqZ7ph+QqYds7+/NFkyXWPtbhsgZcB7sd++ftvj2hcXccuYXB/xxUzd9xQxc9BXajkDuKsEdbdtMkfO1OOZkz0DeQQNhEuaPkGzO+lfkLpP0gyVZA1JGphW8jQmaWCaqKlFyBxR4/pOm1Xj08ccWQNhpVBD4wnNroF8wgbmTzy7HtvQSVJX+xjSBsoUNxBP3qQQNzBD3sDBd0hqeQPxBQ4sm4UD5UgcKF/kwHHJHIgrdIxtYMIlA801XGB/0nU40ZomY1zcRtuBCWGM7JixOFP76xMyF/0Pje0iZpsuIdPV32Hb/bHGiRtPykBYCdNF/Gaww/u1F9+jjAnyiJm6r4OxtPtuZc2Av5zpysoYyxi66NbvpKFLzoDnfSNO9gzEEjRQYhYNlCNpYHo2jTu2XFEDkWUN9AqbEFnj+vLoKmJ2Daxb2ED50gbiiJtQaQNlixtYXt7AMgIH1p+FA2VJHIgncmD/3KckkQNp1mWp7+9RLLJra7h4YcIlE2OXUr5ol17GdMf2bTstO6YZJyQbZ2khc9F2PJtlTtyx2GPCYo6U6e87TMoc9BG4vozrJ46YgeEFgOu+umj33578L5I1Qxo5A33viZ6TggiCxo2le3s0QXOiSN8ENXjBYFha0sD0bBqYJmp8JchcUePGkCarBsqWNUNjKl3YwPqkDcRZ1wamSBsoWdz0HZdS3sD6sm/Ou84scKBciQMmcnyxRXaNGhMuGWhnuAyV4jTbxJIxsC9k2rFjyZjRyfOEcqXQPmMImTru8Pim3peYsievlLnooxkwLFsGfLNFxr/EDkSJ98R7YtnPjKyZpeQMwElXhk6QnIEQQdMnZ/REe/v1ea3tE0/QQNxSJ5guaYAkJU+QToIsKWpcm8NtoaLGt68uWeM7gWlP+OYIm6myBgInkh3vkbnCBuZPJIce81LWtakpWdxAPHkzZyK/NnkD6xQ4EEfiwL7IKUHiQBqRE1PibBbBMlw8MOGSg4OSInfbNblJIWN2p7onYIZkTBe+pUrttoftB+r4Z0mMobbLCJlmvyGZLD6xp2bJ1LFSSpn+MaTPlgHCypjqfiaKmcnlTB19QrecOSxnAt+SJkgjZ8D/fjrSZs+48RxuiyloIH4WzZokDRyHqIFEWTVQjKxx/fodv1VhA/GkTV/7mNIGtituuo5NnXUDM+UNmMBpEFvixBI4UJbEMY4bEy6Z8M1wyS1jgGilSuGZNH7lSn6x/NrGEjKHfc4rL4qVJdMV+7B9XCkD07JlDoOOZ0+0BUGUMqa6ry5JEnGdGYibNVNtPdhSgpyBdQma3Un/495H7CwaN448kga6Tzx9JQ1MEzUh8mOqqIHCs2ogqqxx/Xp2OyO7xrUtU9hA2iwbKFPaQPniBtaVdXPed0SBs7S8gfwLGTeJJXCgTImzPWT+i/YIMOGSgcNFc4eFSt2mzRwZ07c/poyZU6rUbh8mY8C3XGms38P2rS/0SELGtY+ZyVKYlIGDk+cYUiZGtgyUIWaqrQdblsma6ek7kZxx5UTd+0KyZ3Ynen51rcMB+Zc3QRmCBgiWNGOCBvqfR7dvWUkD8bJpXJuu4/2yac7Hs7KsGt/+XMM8ssa1jVsOBcsKG8iXZQN5pA0sJ26mSBtIL266jj1WeQPTBA5sMwsH0kicVSMcfkEZB5hwyUHHe3SqUAk91jcrpqtNShnT7m+eFIGlsmMO28cTMiGlRY7p9ytF/BhSBuaXMHXiMRk/JjED/nJmd9qdZeF7pSY3hrCTnxilTYNyBqKtP+PG1dF/ZEED6bJoIJ2kgTKzaVyM5UWNG0+6rBooR9a4vj27TpBdAxMmga33VyxhA2VKm65jypI2sLS4gXjyZu4kfXF5A9mzb86HUZDAgbgSZ/VYhssoJlwyIHAgJ5q05URMGTMkNXLJGDi8z21yZceE9h0qgoaEzBjps1jmxe/qI/y5gAO54FHCdDLyOLuxeDzWE8qYwFc++H1BLSFmYF7WDPiXNEGgnDkdn6DvxY5U2hSaPQPLChooK4sG1i1pYJqomZKdsqSoce0Ot00Zd0ifOWWNa7t9YQPzJ30+z0nJ0gaOR9y0j19a3sB2sm+gPIGzWkQsw8UDEy6ZGJIafXRJiT4Z0zuJKFDGwKGAKTc7BtYiZLr7z5slEyp9HOuXMq6/8GwZR3oxs+sp8fHOmjlVpOP+zC1pgrRyJmZpExCtvGntggbSSZohQQPzJQ3EzaZxbbpi5BM1bkzLZtX49ukaTpc1kEbYxJA1sD1hA9uQNrAucQPx5E0OcQNxs29yyxtIsw7Oakic4SIiHw98O/AlwCcA7wV+B/ghVX3RhHhfDfzMSLMPquqtQ2P3YcKlQGLImP5JxHhWTNfxMRbv7Y6bV8ZMa9//wbI73Z/AxV0/BkoXMu1jul5vsftwLCNlusbj/ZgnzZaBZTJmYE45U1//ML+kCdYhZ9yY4pY39Y3hYnwdY0goaCBuFo0b03ipk9s/T9JAGdk0rk1XnPJFjWt3uG1qVk1Iv65xewKcV9a4MfgfvyZhA9uXNrC8uJkqbSCfuIFtyBsoQ+CsBiFphouI3Bt4MfAx1ab3AXfAyZcvFpEnqer3Twx/BXhPz74PTozZSdHCRUQ+EXgq8DDcA/124LnAU1T1Lz2O/2rGDdZO9eJUT0SuA9420P6XVPVxY30Por6TyH1SZ8X0jSHl4r1DbYZkjNsWd92Yvn7C2qfLjpnS//JCBmJnyXQfsy4p48bTKgc47ciImJgtA8uIGTicLIeIGVg2a6ZEOQP5s2f6xnAxvo5xJBA04JdFA+lKndz+MiUNxBE1U7NSYooayJNV49vvReP4ssaNIWAIibJrYHlhA8tIG9/naYq0aR9XtrSBXOIGysm6genPE8QXONtGkmW4iMgtgf+CcwC/CzxeVV8rIrcF/hnwbcD3icj/UtUXTuji5ar6oGgDHqBY4SIi1wMvBz4WeB7wBuC+wDcBDxORB6rqu0fCvBp4Ss++vwE8BPhvPftfg5M7bf5gpE8vurIofKSETzxfUpcoNbfFljFumwzEDr+8de7sGMe6hYyLmbZsya+Pqf2kkTJwKGYmlzCB1yRbZy9uG1fMuDF19T2vnGlwDCuRMzCUlZgve2Z/fPEFDZSZRQNlSBo4FDWpJI1r144zLSMl1iW6a1KOv8ZkTd0+srCBve/IUoUNbE/awPGKm2aM3PJm8wjzXiDDfD1wF+ADwMNV9U8BVPV9wBMrV/Ao4PuAKcJlMYoVLsC/xcmWJ6jqM+qNIvIDwLcA3wN8w1AAVX01TrocICK/Vf364z2Hv1pVbwga8UT6TrTHZEa7TUjsIXxFTL19CzIG5mXHhMqY/mP8y5VSjKFEIdM9jpz9TJAyMHpZbPDLlul6DuZky/iKma7JpOvXU8ycdl9RZ66YAV8B1581A9uQM26f32PhxjWSPQNHI2hgu5IGlsumcXGmlw7lKH9ybff/nptVE9p/CbLGtT9OYQPLZdlAXmkDxyNuIJ68sctAZ+ErqttfqGVLi3+JEy6fJSKfqqpvWGxkgRQpXETkrsBDgRuBH2nt/i7g64DHi8i3qWpwjZWI3Au4P/CnwK/MG206UoqYoWOHiCFjxtaLaW7LIWOA5KVKJWTHLCFkuvoYY4mypa5+uo9bp5Rx44qXLeP6nV7GVKqYqcfWNQbAW84MiRnon4j7SK2LMYXJGTeueKVNME3Q+MiZvrFc7OsYSyJBA/MkTYiggWUkDSyXTQPrFzWQN6smpH/XOI2scePwH4ZrP31y7bWOy4aFDSwjbZrHLiltII64mZtMkVPcbBIRuOxx0h0cVm4DfHb156/2NHsFbgHd2+GqVky4BPKQ6vaFqvunAKr6fhH5TZyQuT/wognxv766/SlV7TudurOIfD2ubuzdwG+p6u9N6Cs6U0VDu82U+EPELlEaOr6rbXNbSTKmq6/SsmOmjMNHNKQQMi5u/OyV7vsbLn/8+0onZfSkO/7kbBnwmkjPLWNaUsy4MUQoZ1ogawbC5QxsN3umayxN5ggaKDuLBrYpaWCaqJmbiZJL1Li2+3+brEmbXQN5hA2YtOljeWkDucWN4c2ncTGpeG1XA1XdicgbcUuO3GNCH/cUkdcCdwWuAn8E/A/g6ar6tgnxeilVuHxKdfuHPfvfhBMudydQuFQL8HwlsAN+cqDpF1Y/zWNfCnyVqv7vnthe7747nQu7+OTKitmdHoqI830JSpT6Yvhk1cyVMV1jmSJj6jihUiOGCDmMGS5kusghZKaIklj3N1Zf3cetX8r09j2jjOmi/3liBvyzE2KVM8H8rBnXxzblDByfoIH4WTSwLkkDabNpXLt2rLJEDUyXNbFETegYUsoaNxb/obj280pXkgkbOP9OTi1sYL60CZUga5Q2UI64WT0CXE7yINyp8fufDbSr991poE0fd8AlVvwlcFvgntXP14vI16rqL0yI2UmpwuV21e17e/bX228/IfaXV8f9iqr+ccf+DwFPwy2Y+9Zq272BG4AHAy8Skc+YUsqUkzkipt0uJP4YJa0XM9T3WLspMqa5zVfGtPv2ydLwO2aekPGXE2PHhAkZH5bPXAnva6oAiillIH4JU4psGdiGmKnH1zuWgZPBWCVNXftKkDOQRtAMyRnYjqCB+aVOkEbSwHqyaVy7drztiBowWbN/TNpyKDgOYQPrkzaQU9wcJXcQkVc2/v5xVW2vqXqrxu8fHoj1oer21gH9/xluiZJfBt6kqjeLyLXAF+DWhbkH8LMi8ieq+rKAuL2UKlzGqN+ZU94dX1fdPrNrp6q+E3epqSYvE5GHAr8B3A/4WuDfdBzr9YlxZ7lPMXlovtkuJawVMxQr9XoxQ32PtZsrY+Dw8ZhaqhR6TL0ttFzJZyyxxND+MfHeWiVlyTimrSfjK2V2pz1Xt1lptgz4yYT6MVtazMDQ51marBlYh5yBDNkzMHn9ma4x7e/rGNfIZ1XMdWhgmSwaGJc0rk3jO9FzIrikpIG4oiaG1EghaqCMrJrQcaSWNW48/sNx7dNn18B8YQNp17GpWZu0aR6fI9tm1Yiwu+R5snvBu1T1PmORJ45olOoS0i9sbbsJeEG1bMkrgU8Gvh94QIw+SxUudQbL7Xr237bVzgsRuQfugfsT4AUhx6rqVRH5SZxw+Tw6hMuWmCoXutpM7ae3/wAZs8R6MX3H+rQbkjFu2zLrxtTbcmTH9Medcky4kFmybKk9nu7j+p6HNP1d9Fm2lIHp2TIQZ30ZN4b1iZl6PBCvpAm2J2dgevYMxBM0bn9rXAGCBsrMooFDSeMjaFy75SQN7IuapSSNa9uMNy+bBuJforumlKya0LGwJ0amiIfysmvcMQHrthS88HCTpUujakrItjEAdynomlsC7+9p91Ed7Sejqu8Vke8Ffhq4v4jcUVX/Ym7cUoXLG6vbu/fsv1t127fGSx8+i+UOUT/gtxpstXFSlyeNHd8bN1JWDJQhY9w2Gegj3rox7f6Xyo5xbcLLlbYgZOr+ppcSxekvrM8ypEzdT9cYeyfJnmVM0D0JDCljgrLEjNsXJ2sGwkuaYB1yxu2flj0D6cqbusbVJreggXBJkzKLxrXLJ2kgTzaNizdf1MC6s2ogcgnU+UH1uh1pZA3MEzapZQ2Uv/BwTY4sm5q52TZrQoGr4RkuPjTXbbkzF26gzZ2r27dH7Pu3q1sBruNi/j+ZUoXLS6rbh4rISfNKRdVloh6Iq+d6hW9AEbkF8HhgB/zUxHHdv7p962CrI2WJ8qSh44cwGdM4bmTdmK7+UmXHuDZxy5X6xlOakHGxxzOclsySmdPnRb/ppYwbT3nZMlCGmHFj7Ok3cjkTxM2agThypvncTbmc9nl/ObJnYHOCBvJl0cDykgbyZNPANFETM+ukBFHj2rvb2KJmyljcQWXLGihY2MCi69jU2KWgs/IGnM8R3EK2B8JFRE64uNDO6yL23Xzio6QrFSlcVPUtIvJC3JWIvhF4RmP3U3AZJs+sF64VkcvA9cAVVX1LT9jHAh8NPL9nsVyqWPcDfldVb25tfwjwLdWfPx9+r46XkkUMhMuY3jgd45xT5rQWGdPcNiSYlsqOGYvtM56+43IKme4xdU+ep2bJzOmzvW3oNV66lKnHmSpbpmt7DDGzJ0FWVM4E8eXMlKyZmiWyZ0LkDIQJmlA50x5bF3MFDZSbRQMmaUpYy6U0UQPHJWtg+ewad9x2hc0xoCKc+bzwQ+Oqvr9aWPdzcFcN/k8dze7HxfIjL4rY/X0bv/9RjIBFCpeKfwC8HHi6iHwB8HrcA/tgXCnRdzTafkK1/49wqT9d1IvltldBbvPPcdflfilurRdwVyl6SPX7k1X15d73ogffifWWmbtOTLvdlL52p93iAZbPigmJkVPGwOFj4yNjuvqcf9xwdkxXbB+xMnZciCCKJWS6xxQnYyVMlE3vM6zftFIG+kuY2uOs+zrs393OzZaBeeJgSrZMTYnlTFB+SVPN6kqbYHb2DIQJGp/FzHMKGohT6gTrkTRgomZM1EBZWTVTxuMOKlfWuOOWK4eCfMJmy+xOkmUC/QJOuHyFiDxVVdtlQ0+sbl+lqn0lR3uIiKhq75MoIrcF/mn15+/EWL8FChYuVZbLfYCnAg8DvghXn/V04Cmq+h7fWCLyacD/hd9iuT8HfCnuCf5bwGXgHcB/AH5YVX898K50DGj/z6ETKZMx+6w1K2ZLMgYOhYzPujFdfU6f9I8f55MdMz324facQmZ4XGnKlnz7TNVvVz9d9EoZ2BMzQ1Kmr6/ZUgaiiJmUZUw1scQMxM2agTJLmiCenIE4Qut8/8KCxud9WoqggWVLnSCPpIF82TRgouaw/cXvxyhrYB3ZNTBD2GwVAZ36BIzzTOCbgbsAzxeRx6vq66rlRZ4MfFnV7kl7QxK5Dnhb9efXqOqzGrvvIiLPBn4C+B+q+r+rY67BJVf8S9wasjvg22PdkWKFC0BV+vM1Hu1uZODyUar6+qH9rbY/xfQ1XrzxlSwmY/aZK2K62k7pKzQrZije1mVMc9vUUqWuflNmx7h28daP8T9umpBpt+siRwnR8HM0Xl5RmpSBtCVM4CdmUmbL7I+lTDHj9uXJmnF9rVfOuP3TS5sgbnkTrEfQQDxJM0fQwDYlDaQRNbGvipRS1MByWTVw3LLGHbe8sNkaSroMF1X9sIg8Elcu9FnAa0XkfcCtgZOq+ydVl3kO4X7VDyLyEeCDuCsgX672fwj4BlV98fx74ShauBwrJmPCCcl0KS0rZkrMkPvVJ2OmSqocMqar35TZMa5deLmSb/xShUxXu2UW2u0ek/dl0xeSMtBXPtT6M1DKuO2HfR2Ogf4xwKJlTPvjWV7MuHEtnzUD+xP1ITHj+tuOnIEE2TOQfP0Z1+bi96mCBtabRQPlSxq4EDW5JY2LW4aogTRZNe4Yd5sqq8b1UbasgXnCJl1yx0oQSVlShKq+RkTuhcs2+RLcMiLvBn4H+EFVfVFgyHcAT8BVvnw6cEfcOjAfBN6Ekzs/qqpR1m6pMeGSAaX/pMV3UtRHfbxvu62zpIgZi9GXFQP9MiZGVkxfnKHXylIyxm2ft25Mf7+H7drHxhQf+22mCZkU4xrLsJgrZNy2OGJkTpbM3L7H+y9LyjT7OxwH/eOAoGwZKFPMtOPNFTNun1/mh6+YAX85034uS5QzUFb2DByPoIFlFwyuWZOkgW2KGjjOrBrXz7KyBpbLrtkKLsMlrXVS1T8Hvqn68Wl/Iz2VLar6YdzFeJ4Ra3w+mHDJgQxnO/Thu46Dz/HHnh0TUnKUsjypjpGrRKkvzlIyxm1fdt2Y9rY+6TF27JzJ+Zz1Y8bGFTo233VIUgqZut/5UmR63/3HD/e93396KQNlZsuAn5gZkzJuTOsRMxA/awbWJ2dg+ewZSF/eBGULGpiXRQNxS50gXNL4ChoYljSQP5sGpomaVBkmpYkad4y7TZlV4/qZmHWxk8VkzVZImeGyFUy4ZEClf4IwRcR0xenCZIwfa8+KGYo5RE4Z09/XekqVwo5NV66UYmw+ZUu+n0Gpy4dCH5e5ffv0vz+GmVIGZi/267Yf9nc4jovf52TLQPwyJjem/GIG4mfNQJqSJkgnZ2AD2TOwOkEDx5NF49qapHHt69jrFDVQblaN62t5WbNWVIQzu2b2KCZcMlGfLAxPQFrHDDxbu0v9k/OlZIxvX2ulFBFTx4mZFTNESPlbSHbNGmVMc9uYcFo6O6a9rTQh09ev//iWWs9luO8p/be3j792ypMyzT4Px8LwWCBbGZMbVzoxA34LAMPyYgamlzSBv5wZm/yWLmdgGUHTN0nfuqABkzTnv5uo6SSlqHHHuNuiZc2KsQyXcUy45ED2Rcv4f0QP93Vt65toLyVjQttuhRgipq/9lD5jZ8XEKFEKjVOqjGnGLEl47LczIZOidGhcpsXr328My0qZoT5zZcuAn5gZmmjNFTOw3ALAbt+0cibIU9LU3l+inHFt5mXPwDLlTVC+oIG4kiaGoIH8kgbKyKaBaaImpaxYQtTAdFmzhKhZMyZcxjHhkoG6pGiJidt5+wkypi9W1zh8jj02GROa5ZIzKwZMxrhtwzKmub3E7BjXbrxcaenxNbfnEjLtvpfOkgnpP8YY2v31UZKUcfsYHg9Ey5Zp78slZtoxc5czQZ6Spvb+HHIG4mbPTJYzULSgge1n0cBxSBpIk03jjkmXTQPbFDXGNjHhkonmiXm6/zQPH3vePtG6MSHlR8dUqpRDxAzFqWNNKVEaizvUn2+cEmUMhGfHxBMW/cf2HZ87O6a5fYokiC1k+trGFCLTnkf/0qUYY+jqs7u9v5SB6SVMbt9hv4fjufh9iUV/2/uOVcxAOSVN7f1bkDMQR9D4yBmYJ2hcO3dbmqCBdFk0sD1JA07UpJA0MC2bxsUvT9RA+vKntaEinF0KnJAcISZcMlBnuAxNJGFcxtRt2sd0tWtvyy1jmmPxOf4YsmNCy41ilScNxUqRFTPGGmWM255u3Zjm9lzZMa5t3nKlvjHGEjLj45xXNtTenqN0yWcMh+PwlzJQRrZM+34tUcbU3uezvowb2zrEjNtfXtYMlCVnoPDSppoAQTM0GT8mQQPxS51gvZIG0pQ8QZmiBubJGuN4MeGSid2pDp7Q+sgYF6cd97CNz3HNbT4n6WAyZkksKyb8vi4pY9x2SSJj2mNIVQrkd/z8cqWxfnIKmb7+fcfZ7j9HlozPGJrjOGYp4/Zd/B6zjAn6J1FTsmXc+MoRM1B+1ozre3k5A9vNnoHjEDRQdhYNlCFpoJySp5rS1qfZOipw9dQyXMYw4ZIBPemaWO2/2efImKE2PpOauSUNMDzhTrmIbzvG1kuVcomYsVhTs2KG4g6xZRnT3D6nVKndNvT4WNkxU/ppbl+LkOlrO1fItLdPeU6b4/B6XW5Uyrh93X3vj2n/75KyZbrHl0/MwPSsmVAxA2lLmmA7csa18XucYXjCv2R5E+QRNLCuLBrYrqSBtCVPEC5p3DHTRI1xnJhwyURYZkq/jHHtw7NjSi1Vmitj+sbXF2Or2TGxRMzQMaF917FKKFGaGi+VjOlq77bFWzembwwlZMe4tn4ZFznGWZKQaY+hhCyZ4Tj+Y2n320cKKTPUd8nZMu19ucVMM24qMQPx5czcrBnYppyBBbJnwARND6GSJraggXVLGihP1GwNFeGqreEyigmXDKjA1Wv2t7VPSodOUkOEzBKlSiXImL5YfWPxOX6L2TFTMlyWyoqB/uc3RVZMs9+5sULj+LZPKWPacedkncQ5Pn25Upzj8wqZ9hjmZslMfW59xhE6Fv/xLCdlIDxbxlfKDI8tPFsG1ilmIG45k9ufP2vG9T9PzvhMXFPKGSgwewairT8D6xE0sI4sGgiXNCGCBvwlDZSXTWMcJyZcclAJl3mSpbvt3OyYMbqETYySBJ8TcRifePcRW8ZMaV8ypYqYOt6WSpTGxrSUjKm3T5Uxze3Ty1p8j49TruTX19zjw4VMM0boZ1WusiWfcTTH4iNkfMbj+z6LJmXgXMzElDLt/vfHtf/33GwZmCZmQqQMTBczkGedGbd/+iLA4C9nxsSM6z98vZlmmy3KGYggaCJmz0A+QQNlZNFA3lIn1379kmYrKHDF1nAZxYRLBtxVirRxgt395dMnZHzbjbWtRrP319zsmKkZLlPS3tcqY3z7LIGYImbsuKljmJoVMxR7iLXLGLc93rox/WPobtsVI2V2jGsbZ/2YseP7YviMdezkvLSypeb2NWbJtPvuI5WUGep/SgnT+PjGJ51LZMtAmWIGht9XKcuZanzlzNSsmWabtcgZ167c7Bk4PkED6yt1cu3zSJrj1DBGGxMuGahLipofrn2T9qEv+ebJrq9kCRMy/tkxc2XMYd/+k5i5MmbJRXx9jlmDjJma3bLlrJi635B4OWSM2+63iC90P45zZUy9fd5Evbu/7jFML1ca66u9fQtCpjmOZUuFhsey9HjaffcRKmVg/roybt9h/4dj2/87V7YMrE/MwLysmSliBsooaWq2WUrOQDmlTWCCxpe1ZNFAeZJmGwgqtpbNGCZcciDuy2AoawXqCUn/QpJNITM02QrPevFtd/GhMuUktR27HWPOf8BDZAz0T8CXXsR3rH1IvzkoQcT4xDMZk0fGtMcSR6aEHX84hnnZMWP9LTXeXEKm3X4/TlyZ5TMe79duRikDyyz26/b1j+Fi++G22NkysD4x04ydUsxAejkztaQJlpMzsN7SJggTNLHkDOQVNLDOLBpYTtIMnG5uA4HdiQmXMUy4ZMBluGjvxKZLlCwlZOKJmzKyY3LJmL5YfWMJibHG7JhQGTD3uDnxUpQotd87fX37xlurjKm3+8iYvnHEKWPpjzE1O6avv7Dslu5+ShYyQ+3XnCUTc0zt/vuIKWWgjBImSCNmxiaDaxIz4C9nUokZmF/SBNuVM67d+rNnwATN+TgSlTpBuKRZMwrsxFYNHsOESwb0PMOl+z8iF0Ign5Bpn1D29TOlnSNfdozvZMtHyKRcN6Y9Hp8Ya8yOmXI/+o7zPXZKvFRZMUMyZoqMWruMge7H03+S393eN0aq7Jg4/XW3KU3I+LbPt6Du8HiWGFP3uCJKGYi6rozb1z2G/fEdbpubLQNhZUzN/SZm+smZNQPrlDPg9/mXM3sGTND4UkoWzdqxDJdxTLjkQC4uC30wgW9kX4wJGfd3/8nzfqy+Nt1Cpil9mn83j623+2S9lJQd4yNjuvptblujjJl6TA7myJSlREwzZkklSkMx1yBjoPvxDClV6htLKXLjou1SC9OGxJgvZIbG0t1nd5u8a7cMj2lYpIyPqblvi1KmaxxNgrJlIEkZU3N/aBkTrFvMuDblZM24cRynnHHt+r8j2/hM4lOUN0FeQQN+z22qdWhgWhbN5hFhZ2u4jGLCJQMqytVr+jNP3PZ9qVDTPkmdkyUTS8jMzcIJaeeIkx0zJmMO+/X77/lcGZN63Zi+OGvIjkkhYnyPnxpz6asoNfuOFbN0GVNvnyNj6u3zJ+QhMeaVKzW3b1nIDLUvLUsm5ph8xtU9tvxSZmwcJWXLwPCEfs76MrCMmIF1Z81AnJIm8JczvhkWJcoZ8D9viZY9A9HXn4Ep5+1xBQ2kzaLZOgqcnVhJ0RgmXDJQX6UIhk7+hv9D1pykzMmSKVnItEWTV7tLujchSylkYsmYru0p141pjickRslCZm5WS+ysGJ+Yc7JixuJPkUsx5U5MGdN3jNu+zLoxze25xcZF2/kiIc1937aQGY4VNqYY42ruC8teO1IpA5OzZcBPzEwpY4J5YgbKyZqZI2agrJKmut3Scga2kT0DccubYJ2CZutYhss4JlwyUC+aC34nZN3CxSeN2idLplwh05YUfSez7Qnm8EnvMtkxfVkuYyfEc7NjhkgpY8aOC+k/JmsSMc24qbJihmRM7KyYvpgxZVDqdWO6xrSG7BjXPu76MX0xQuNMXbg1tZDpOqZvPFMfhynjGo6VThY1xzDEFCkD+UuYIM+iv839xy5mYPy7dI6YgfXKGSi/tAniC5rY5U1QhqDZMm7RXBMuY5hwycFJU7j0nXz6C5WpWTJ9McaETLt9131YWshEyaKJmB3TRYrsmBAZk6tUyefYpWVMiSLGJ26qrJgxti5jYFjIpMyOSSE1XPtly5Xixun+TrloH39h36FjliwRChFLoeMaireklIH1ZMtAnjKm5v6tihkYf5/GKGcCkzNNYskZ1zZT9gysUtAYx4sJlwyowNXL1R+Xde+L4LTjZCynlBnLkun7sMkpZHxES9cYutqVkB0TS8bAwMQxgoxpjic0TinZMalETEiMqXGPWcaEHpNDxvSNZwmpkSI7ZqzP7n5jxVlX2VLXmJbMkhmKN0XKhIytPY7+49YhZdz+w225ypia+49ZzLg287NmwF/OzBUzEF/O+AiImHIG/L/nY8sZyL/+DMz/Z+zqEeHsdOZ/G48AEy4ZaJYU1dQfWNp1aS0PKeNidImNaUKlMdrz7TGzZMaETLNNqJDxyY6p242JFh9pA/GyY0JlTN8xvsfttc+4bsxYnNxCJkZGS66sGOh//nzL04b6WDqrKTTTpe+YNciYentYxkH/thjZMa69CZnQ9jmzZIbixZRFPmPrH986pIzb3z+emqWzZcBPzEyVMlCOmIHjy5oBPzkT60pNNTnlDPifH0UXNImyZ4zjxYRLBrqEC4CcCaC9Hwj1B9XZ5e4TAml8WPtLGd37AJwiZaZInTEh02zrU/MfMi4fIeOTfVPH8smOaQqZoedkbALbxRLZManXjWmOZ0qcXEKmM4soUKAsLWKasVNmxSwtY2JmVy0hY8BfyKxFaLi2YRkTcaRBaEZIHiEzdEyK8qAYWTI+Y+sa39alzNh4kkkZKDpbBtKLmbqP1GLGtZm/CDDEzZqB45AzECZo1pI9szUUuGpXKRrFhEsG9EQvSoqaXHby46za1/WBKedfZMNipk/KdGXLpJcy046/GJf2HDM05vxCpi00erNoMmXHLCFjSsiOiXF8CLEESioR4xs7VVZM3c/U+xdr8j4Wr++4KTLGxVpndkxonNKyY+p9YY/DMkIm5JilpUfI68NnfCHCyGd8fftySRlYtoQJ8mfLQHoxM5hhUpiYgWWyZiBeSROUL2cgX2kTJBI0xtFhwiUDKnDzLXacND5wpOcL/eAL6fJFCc24mNEqxuH+wWyZzFJm7ITTN0tmDUJm6KSyS8jEzI4ZO9HvG/uQxOnaVnp2zNjxoWMJjRFLxEyJNSX23KyYsX6GWKuMcQRmhSwgY/qO6dq+RHaMa192uZI7Jo6QGRpX3zFLSY/pwmPa2ELH19xXhJQBOIsvZdz+/jHVpMyWgfWuLwP+YgbmrTMD/nJmyawZiFPSBP5yJtZiwDW5Sptc2/jZM1tCRbh6cqTpPQGYcMmACly5RrkQIgK1PGl9CJzVgqUlZ0LFTN+HpI+Y6ZMycPGBX4KUaR+7P57hLJmcQqav3yEhcwzZMUPEkjG+sXy+bKdKhK7jpoqTUrNiYL6MGROKKaTZUjJmiXVjQseVIzvGtY9XrtTXR9f2rQiZoWOWzkKJnSXj9q1fykCeEia3v3u7j5iZKmXAT8z4TNJTihlYZgFgiCtmYJ0lTXW70uUM+J8/hGbPGMeDCZccyIVIgYZUaYiXww/HfTmzvJipjz/ct5dt04zfkjL9xzfHtL8I7pBYGRIyvsdefDDOK1uKLWTq2zkZNLBMdkwOGRPrqkrNMc2NFXPCH1uc5BAx7fi5SpTa4wiJO0RMGTN0TCwZU+8rrVSp75iu7aHZMWN9h0zCTcj0j22pLBm3L7x0bzxm+BjrsYzRN54c68q4/WmlDKTLlqnb5FxfpqaUBYAv2i2XNQMmZ4aYek6xVlSEK3aVolFMuGRgJ3DztXr4Rr98mGnS/iDakzMV+x+Qw1kzc8XMWS0DJpYx9YmZsBKmdFImVZZM9wdwd8ZOU8h0EUvI7C5Vj6OHkAkhd6lSrHVj2mOaE2cslm/Mg8dg5ndcShHjGz91iZLJmNYxhZUqjcWKlR3jjglb4ySHVHLHlClkmmOzLJl2zOHxDFGilBkaV03J2TKwDjFT9zVXzIDf91UsMQPlrjdTt1uDnDGOBxMuORDl6uWLkqKm/Dg5g6sdHxKhcqaZQVN/uXaJGRfjsIxn7/iq7y4xs3d8M2tnhpg5P3YgWyadlAk/zjdLJrxsKVzIeMuWgTY+2THN/teWHTNErowW35jt2F19LFU+lCJ+u4+5WTE+fU29ryZjhsdWWnaMOyZPuVL8WNOETFc8n9fxFrNkxmMeh5SBbWbLgImZPmKKGYgnZ2JmzcB65MwWUDmu+zsFEy4ZUKESLudbzn9rvkn3v5AOBQj0f1Huvdk95Ux/SdOhnPHJmmkTLmaax+7vO2vel1bcoRKmHFKmKWS62p903ZeO/T5CZl9mhAsZX2EDw0ImV3bMXBmzVHZMaKzQ2KmYmikSo4/YWTFjfc25r2uWMS5eWKnSELFkTOpYscuVuvro6zt+rOG1PkIXX02RJbOk7Ah9f7h94xPOTUgZmLzYr8+45kgZiJMtA3nXl4HtihnXrtysGShfzqwdBXYDFwUwHCZcMrA7gZuude/u0wML2pARjQ+ALlEAYYJmVM5cboqVRt99cub8+MM+Q7Jm2kwVM17ZMpebj13/cRf3L42U6V8cOGbZ0jQh0z2ui22h5UpLZsekLFUaI3SSnaKUJSelZ8WA/3M7NSumPZbQ2EMsJWMcgZkgC6wbU++LV3oST2K4Y0zIxDom9jotKbJk3P4068n0jecibv94xihVygyNrWZytgwkL2Oq22xRzMB6s2YATjxlgI+YgTRyZu3sLMNlFBMuORA9lw9nPZkr0PrAvNSQGbtu+TJd0ByO4axjYd694y/vfzDVseoPoq6SJnf8YZ/trJmpYuZKI8bhB6JWpUyHX857x03KljnMXOkTLCetx9z3mDlZMiUJmdDsmBAhM5QdM3QSW1qpUnNMseLlJLWICekjZ4lS13hCYg9RsoyB/sc9VMYMjS1ndow7Jt+CvmOx+o7pj1W+kOk77iJmePbJeEZL93H9MaeNczxu/3gu4g6PaYgcUgb8xEzqbBlIW8ZUt1mTmIEAaWFZM3v4ypl1I+zkSMzSDEy4ZEAFbq4yXKRHnkArS6SZZdIjaeIImv0x1OPrkjPN/gezZ2bJme7H5ORs/7jmWNpfILHEzJRsma7HdSzrpU/KhGTXhGTJ9EuUwwWGYwiZ8989smOgX8jEkjHn/QZmx4TImJilSu1xxYiXg5wipt3PEiVKIeMJiT3GFmWMixc2tpzZMe6YOOvHjPXfN4atCZmx42KXLdX7p0mOoZjTSpfG404TSPWYxpgjZWDaYr8QR8rAfDGTuoypbjNnfRlYXszEKmcCv++8XFkzIXJm6yiW4eKDCZcMiMA111QZLrv9D4BdQ47sGtLkautD4ELUjEuasx5JAo0P0UutOK34V9j/gpgiaLyyZyLJmSli5vy4UTFTZb90iJmDbJmgEqb4Uqa7dGk4S8a/bOmw71hCpu9ksS1kYpUrTcmOKbVUCaZPsktgCRET0s8SWTEh4wmNP8SSMibmIr71PsuOCR/DMo/NskJmynE5Li/t4obFdPvjj7Xen0PKQN7Ffl2bxNkyMLuMCcbFTIxsGYgnZmDZdWYg5Psv3n0EfzmzeQTOTLiMYsIlAyLK6eVqDRf2JUtbYGjPh2CfqGlKmh2wa3wgRJU0HoKmGa8tFNpjcl9eh32edVza+vy4HjkDh4/P/pWa/LJmhhYB9hUz0nhs6/GdPzaBYmaalJkvcfbb7h8zHHtYyAzFGe9/mpDZWnZMbBkzNeYS5BYx7b5SZ8XU/c3JihmKPyd27OOmyBgYeF9ElDFj5M6OccflWT+m3pdbyNTHDVFC2VK9P0WWzNCxbt/GpAwUX8IE68qWARMzrl3crJmtYxkufphwyYCcwDXXXHxCamvtlj0B0xIbdRnQNFGje2VEdV9tSeO2N8a7O5QlbUnTFjRue1NYHO4/bcRtCxro/jK8EDSH8c52XeU19XGtvr3kTMeY6xrfVv/95UwXv/uImbnZMrmlzJiQOTxmfx2Z9EJmfnZMe1/O7JjYpUrtscWKmZKlRExIX2uQMaliLyljXMx1lSoNxZsiQ9xxE0q5oma09LdPIWTccd33K6aQGTuupCyZ8bhpSpfq/SZlhllDtkzdZstiBvJkzWwbYYfVV41hwiUDInBpT6Qcvvl3BxLG3XZ9njSFzflxl7qExMXxu55slz5RUx/fNT5fSQONbJIRSdOfRRNH0Fxp9HWYPXMQ5vwL0VfO9IkZoEfOKKdncpj90lpkuC1mDrNluu/LoZg5/D2ulDlsf3i8X5ZMEiGzULnS3OyYOTJmjJj/0Y8RNzYliJiu/mKUKA31OVai1DUm39j1saXIGEe8dWPGWKIcJ3a8JcuV+scQN547rvt+rUnIuLhlZMm4/fmkzPD7f91SxrVZR7YMbFvMANEWAIZyzndyo5bhMooJlwwIyjWXDz/R9iRLx4eLtg45025p0o4VJGs6RA10yZqO8e19cF7sr0VNM5umeVe6y54uqD+Qr6CdXwQXAqMvi+ZCTLj21W2voNHDBXMb42oLgfaYT8+6xxoqZ3zETHM8J41yqRAx4yNl+o6dImXmZsn0CZm+/rpidLXd6yNRuVJodkxoqVJ725IL+Z7HLVzILCliQvpbKismZExT4m9ZxsQuVYqZHTM9ngkZn5hTBLvPcWvKknH7p413LPZYdtfYvtKlDIyPsaRsGYgjZnzZupjZMlZS5IcJlwzICVxblRTtPD6w2tku9eT/dEi0eAubnv8CtYTNXFnTjrkXx0PUnPezN0Z3O13SaPfCuTuZJWj6s2dC5Yx2lEKJa98WCzuZLWYO2+refe4SM7GlTPwsmcN1ZPbbHz6n/W0bvw+UK0H3pM2yY6o+J0+e01KKiOnqM3VWTN3nnKyYlPFLkTHQ/1xMKVWC/jGWkB3jjotXrjQ2jtiSaThmGevIjB2Xa8Hcqe+rOVky47Gni6R6bGOkljIwP1um77y9yexsGeDMQziUJGWgbDFjHDcmXDIgolxzef+dfNaaiDclw2nrg+EyoNpuP9xnl7TpEjbnbXs+jJrS5kwHTsSa4+8ZW2cp1N4Yu+PtxehYjLctakKzacYzaVrHtASNa1vdtj7821dxuiiN6l4j5VDQ6F78iz665cx+zK77oHttm+39xIx23Mf2l/l+m+Hsl3bJ1mFbHykzR8jsZ5DEFzJD5UpDssSyY/IImaVFTEifsbJihvqs+zUZU8cMlzFjTJ3Ax4y3VHbM0DhiS6Z5MbchZFzcdWXJuNjLrydTj22MWVIGtlPCBKssY4I8YmarKMJVSXhCtBFMuGTgBLjm0m5PWLSlSk17QV2AjrVc9+iSN13S5rwPbYqP4dhN8dHup69dkxBhc3lgPHNlzZioaXfrk00TKmn221W3kwRNqJzpWkRYDk5k9sc+JGYai/l2LP47J1tmvpSZliWTVMhc0yc7/ITM0GS8Ly37GLJj5sYOpSQR09XvEjJmrESpa1wh8evjS5IxsRfxHWNZyRA7Xv5ypXQx1yFk3P51Zcm4/WlKl+r9OaUMbKeECZbLloF1ixnjuDHhkgOBS6e7wQd/V8uIxgdCn8RocqaHcqXJwRWRJsibmsuttr7iJrW0qWXN0Djqx2G6rDlsv+to3ydqui7FffG3v6QZEjT7ZVG1UNGDtq79oZwBF99XzvSJGRdnX8y4+yct4VLLjSExM54tU2f8dGe8tNuNS5nhLBn/xYTH21W/dAiZkzP21o9pM1auFJId00fp2TFQhpDJIWJC+12qRMm3zZw+pj7ec8YVOztm6roxLmbPcYVkxwzHXK5caXgcKWKWI2TGjk25Lss8sTG0L13pUr3fpMx5lNEWJWXLgJ+Y8SWmmNkiKnBV7CpFY5hwycCJdC+a25QPfeU+0JAxDWoJMSpxWh8cYxJnKAvF9esvcLrkTVvagH+5lI+46RQmHlk25+VIHX0PZdcMyZquK0Cd7aaJGh9J45NF086gacbrFzQXa9a0s2fG5Ixf1sx+SZevmGlKmfb+drZMmJTpX5h3apbMftsYQmZ8/ZilypVKzY6BvEKmNBHT1feSJUpDbXwfk9hSwSfu0jIGhp+XIUrIjhljqXKlet8xC5mpx5ZaAjQ3S8bFNikTQ8qUli0Dy64vA+m/y411Y8IlAyJwzaWLT5XzRXA9BGFbRvTRJWWGskn6jrvks6jvgSCZLnH2ZIanvOkTN+CXdVOPN6a08RE2c2RN30LEQ6JmqqTpyqLpFjTdcsb1fyhnXLvWGDvljO61uYgxR8x0S5nmbf17CikTkiVTmpCJnR0TImNSZsfA/IyLKeQSMaF9x8qKGeq37ntuVsxYH0Mcu4yBFPJkuK+lypXSjSVFzPhCpj52iKXLlnxij5GydMnFNykDK8uWYdkypmNGEa5YhssoJlwyICiXGyvJXm58uPQJld3AJaAP2x5uO9tJUqETInNqOdK+atJQ/CGJM1fgtOVNZ5wRcTOWbTMkbUKEjU92zZCsCRE1Xdk0Y5LGN4vmIoOm2Ub32g1lz3TJmSEx04y3v87MRfZJiJgZlzAxpUzksqXIQmZudozPejN77TNlx5z3v1CWTJ/4WoJcWTFDfccqURrrI5VQWZOMcXF7YiYQWWliTnzMFr5/WxEyY8eWmiXjE3/o/edij2RSRPhOyC5lAJ+7UYqUgXWXMRnbw4RLBkTg2kaGi8/aLG16S2FUOi/hfLlj47jcGaZP7IwxJnVCs3Pa7X1FzhSJEyJwxuRNqLgJkTZjwsZH1nRl1bTLn0JETaikGRc0dX/hcgYupNGFnHFCpDtrZkjMNIWInLdr3tZrz8yVMv2lS9qxrR37MG5MIdMVD8KyY8aETFf8PdmTMTvmvJ+EWTI5s2GmjKGUEqVmmzl9mIyp4/bE3ED2iDtuueyYsbhjpBAyw3Gnjcfn2DliY8tZMj6Mf4ZsS8r4vIYhnpiJlS2zVdQujT2KCZcMiCiX9maI/sfWE+vQ8+teuRIge4YkT02X2Bkag4/cmSJ2hqROSHZOs+1ckdP1+I0JnKHsm6Gsm6EyqYM4A9LGR9j4yJqrOz9R45tN05Y07Sya9mW2fQVN+1La3XKmIV8OsmbmiBnt2T4ubpq/T8mSmStkmqIoRMi0J8o5y5XGiCVkIH6WTM5smKEx1OQuUfJtM6ePdEJlzmRwxgR2BaVKc2IOxS1JyKQSCeOT2jRCZs6xZV+5aCx+2aVLLsYyUibGujLgN94Ss2W2yM5KikYx4ZKBE+Da1rt37GpB9eT60kSJ2Cs2TmAXaCbHFtJtM1f2dJbceIzhYPHd0cyaVr8TpY6v0PGRObvzbJhwiTMkcPrkja+48ZE2XVk2fcKmS9Yo3TJntxsXNVMlTVcWTVPQtNee6S5tuth3vr0lZ5piZj+u7mf2DIiZ/myZKVLG/6pOw23IJmRSZseMEVPGQJwsmRIkTE2urJihvpv9z80cSiljfOJPOc6xbHbM8mU3fuMKieniTnzcCruPw3HTCBkXe9qYxo7NnWkyX6RPF6Q+8UuRMrDMYr8QT8pARDGzMZTweWQoIvLxwLcDXwJ8AvBe4HeAH1LVF5UWtwsTLhkQUa45OYuWguUjH0IlyX78/b+bC9T6rvmyH686puPDMuRN63ufOjNrPGTPQcbH2BWbAto3H9MhseMjdTqzWEZkzpDIad/vPoEzRd4MiZs+aTNF2Ghj300t8XMhUloiZkDUNCVNX6lTl6ABDjJomtkzfnKmsb1R/tS8MtOhmLlYe6ZbzPRn0+xva7YJkzKDWTIH2+cLmc7Je8CJ/Rwhs1S50l6fE2RASRIGwkVCzKyYof6XWC+mjpFLxowdm0LGTL3ENUx/D5W0too7Ls2E34SM37Epy5ZKz5Lx62NbUgZirSsDsbJltockFS4icm/gxcDHVJveB9wBJ0m+WESepKrfX0rcPoJOU0Tkjqr6F7E69+jvE4GnAg/DPSBvB54LPEVV/9Izxo3AXXp2v0NVP77nuAcA3wncH7gF8Gbgp4FnqOqsrzABLkdaXWmnMvb94Y2PwJgriYYkxBQp1JcZNCqCGh+KfWPy+QDxesxmXMVp6PFSz3Zjcmes1OtAkgzInD6REyJxugROU7p0Zd20xc2YtOkTNspFhs2VjjKopqyZKmrGJE2IoBmXM/tipo7vbhuSY0TM+GTL+EgZ7ywZ2ieG5QmZg74KypBpstYTwLVmxTTbzO1nazIGhp+rIZYuK5oTd84Cp1MXjp0+nulxh2KvVcjMKVvyiT/G3CwZkzKH+DwlS2fLbI1UwkVEbgn8F5wD+F3g8ar6WhG5LfDPgG8Dvk9E/peqvjB33CFCT0VeISIPU9U3xeh8CBG5Hng58LHA84A3APcFvgl4mIg8UFXf7RnuvcAPdWz/QE/fjwR+GfgI8EvAe4CHAz8IPBB4rPcd6Yyv3KJx1jE1+2SHcBrxNT4lcyXmm8x3sd4+Bq9WNGGcfeMJeb66hJDPY3twlaaR8Q/ed0/h4yN6xiRPfX99xE59fJ/Q8ZE5XSKnSzDtCZkOgdMlb9oZN01x0860aUqbK2eH23xkja+o8ZU00wQNHZkz2hAw+3LGbe/Pptm/PYzV1e6i7dD+9rZWiVZrUtcVD+YJGZ8FgM/bzswlTSlk1kasrBgwGTP1eJMxabJj3LETM4uSjad/3xhzxFMuITN2/DrWYhneP7d0ya+PcY5XymwLBc4k2f3+elzSxAeAh6vqnwKo6vuAJ1au4FHA9wEhYiRV3F5CTzc+CfhNEXmkqv5WjAEM8G9xsuUJqvqMeqOI/ADwLcD3AN/gGev/qOoNPg0ru/UTuPffg1T1ldX2J+NSjx4jIo9T1Wf73pGDPlBOG5dRiSlNxvAuAWo1S12fB/4io28slzu39jN6taQZ9zlUHvVeFclzDJ1Xdgpcsybk6lFDj03nujIjiwoPyZ4hydMneLqO2ZMqHVJnp9Irc7pETp/EaQqcvsybtrxpipurtfA5k1Fp0yVsLl05lDXNbe74C7FTfxRdvrkWPvuipk/SXLq5T9A0M2SaImZY3Fwc58RMO073375iZv/FPiZm6v1jJUddbbuOHYpxcjY+8W/uH5uUHrOcCcmKgf7HMlWJkm+buf2kkjHz1xYqS8ZAmiyWVHGnyphU45kTdyz2HCGTcpHqseOdDJ3+PPnFH94/J74jvZCJcT9cP/PHCsNSpi5bkl1/Gx8Zs2USzhG/orr9hVqKtPiXODHyWSLyqar6hsxxewk9rfh54CuBF4nIV6rqfxpqLCJfDDxVVT87pBMRuSvwUOBG4Edau78L+Drg8SLybar6wZDYHjwGuCPws7VsAVDVj4jIdwIvAv4+MEO4wLUy45qMc2i8J3Yhl0dagKkZLj6Xa5tDyDo1MT50hkTJ1Pi9a8HMEDptDrJNxr60m2VLniVRY+VP7fszJH/6yp72SqY6tnVJnrbgacudPrFzdi5++oVOU+ZcuXqyN/YuidMUODdVwqYuk6oFTlPeXL6yL1r6xE1fhk0tbZrlT6dXLsSQi+W218JmKKNmOJumvf2whGnsSk/DwmZ8nZq97QPlTV3HjW3v27/39zVhx4buP2ZCy69SlGv5xIzR75wYc0sWBo+duMbLYMyJ2R3Lx5z2uG3h/qUY59TjUjwPPvunHjvr/Zbgeb44fvq4YiyYO8TUy6L7ckxXK1IkyXxSRG4D1P7gV3uavQJXxXI74CG4apgscccIEi6q+neqNVG+E/gPlfD4N+12IvIgXAbK/SeO6yHV7QtV9wsLVPX9IvKbOCFzf5wAGeNaEflK4K8BHwR+D3hZz1osdd//vWPfy4APAQ8QkWtV9SaPvg84QbmFXF0ka2SY/jPvOYvsTiZxl5Mf747DclxzPpUg85IpM+7vXNnTeZUqj2N1QMTsxfIoHet6vs8zZwZKq7oybYakkPZk2Zxn5KgcyJ+zhpjZG9fu4u/6U7SWPV2ipyl5zs4uJE4zW0d3cLWK0c7QORuRO81SqnMhs5POEqr2Ojd95VN9WTmuXXdmTlPKuPh9a9ywd9u1r69dX9v2MX3bhv5rNyhjPJgqgQxHLimznIiJPymec/ySE+NSJu+xJ8FTH6e+SePUyW7s19bQpFbnSIiB+xc6zrMZ49Ch18FJGskyZ7ww/NqYJYZGXnNzBIfOeCwhjXhfG4nmRJ/GxQzstZ39qu5E5I24JUfukTnuIMGJs6r6z0TkbcCPAT8gIndR1W8FEJH74UTLg3F3Zgf8xwnj+pTq9g979r8JJ1zujp9w+Xjg51rb3iYiX6Oqv+bbt6pere77PYG7Aq9v7hcRr3ftnT77k7lEnEVzk5HbBRlA+uwdIOi5ziGafEl9efMp/YY8Xj5C7eCS4z3xx2TT2cB+7RFD7f7PpU2r3U6lVSLWMaZWHwfZUa0spLOWaNprc347XFp2Prad7H369pWXnf/ucSUvd/zFOLoYu7T6Qby9MQw29Y7ZNY6U+IynJE5mnnjH5tRjPEuO+cRjou15GhQFn/vu8xhO6nvq/28ijec04eM85TV1Emk8EiBzmk9BrP7bTH39pBrPku+v6fc98kAG+wof45KPIcCL/96i3WUhUQLBnRq//9lAu3rfnQbaLBF3kEmVyqr6MyLyJ8BzgG+qSoBOgC/mQrT8EvA0VX19f6Reblfdvrdnf7399h6xfgb4dZzFej9OlPxDXFnSfxORz1XV1yTquxMBrvFaomk7yIZW7j4pXZZF4CTC83Wq6Z9zn3FOvS8hx520Vvgde72ftB6b0572zXZd46n7be9rx5fq7/Zr90R173mqj2venxPVvfjSanO604t27eP0Yp/s9ved919tP1HlZHcRu/5dmuPb7c7He7LbNdorJ834u1Z/dbudcqpd2/e3nTSsSR2nue307PAzwB3XvR3otibNtWX6TM35/tbxZx6vT19Ts3fMhPdLx+OxWk5nZBKmnmn4GI9QfBaRm9uvz+MydzE7jzHuZj4/uwX6OJvz+hvoXyO9dobu39z7fhGnf6yx+jjzMDvx7k+cOD7P4RLPwUHbSAumho49ZIw++C78GtrvvaYMZvvcQURe2fj7x1X1x1ttbtX4/cMDsT5U3d7as+9UcQeZfO0EVf0fVZnO83BX8AG3itUv4dZtmV3vNED9rhg9M1TVp7Q2/QHwDSLyAdxln24AvjRG3+q52Mcn3ueT9TL7xf9LTE5LIsaEfomYU0g9jvbkvo+5kqs9aW/SJwhCYvk+Tu37O3Tc0Jj3Ju4d0qx5bPv9uCc+2uM5lxmHUqJ5zOlOD48ZEBTQmOx3CIrzY3okxZicaEqJWjKcVjFqcXDp6u58HCc75fRsf//J1UocX6kXmalXjN3VKSeHt9C/r5YIdZx6W32fzxrt2rHq9s12vr/Xf+/FaO0batvefv73rnv7QfyB93Tz2Kse7/0+ERNTivjIntJYcnV630lzrDFdGulvTj9j92WqIBga05SYHvexM2pfXx3xRkc1NO4pz0Hv2NI8Pv79FxprDfGmHDNVosS+X6liDgmMlJ9dU/tMIb5XjAJXw5c8eJeq3mekTaov7Sypt5OEi4hcBzwJ+Kp6U3X7u8A/UNX/M3NcdRbJ7Xr237bVbgo/hhMun7d034JybefyMd2UIhLa+E78+1gq62VoEt4mVCTM6Xdy5kXP4x6WkeHXti0OXD/Dz/uQuOhr13ufRjI8OsfXyrxox+oVJh2ZHl1ZGfXvfeKj7mMvS6IhPsYyNNoipL6tMzNq4dHMxJBGrF4pcqZOTuwaYqNLjEBDnDT2Nbc3RUhbYnRta8dv7/MVJDVnu8PtYyKl3eZgW488aUuOrmP79h/s85QsQ7HmiJOrMz7fSslimZkBsMelxOddS4sXn/7mPn5J5c3E2KmE0+DkshDRNBZz9NiVTMpziL/RYzOJgmOM7RN/7vExsoNifj8ZH2j8fktclUoXH9XRPkfcQYKEi4h8EvAdwOOrYwV4CfDDwPcDn4m7bPQXqeofzRjXG6vbu/fsv1t127fGiw/vrG5v1dr+RuA+Vd+vau4QkUu4S2NfBd46tWMBLuu0qxSFyIM2MWXCEL5jjC2SxgTQ3P6mPPZdQsCNxW/y0tWnTzZUZ0bJwOPTbj/0WPXepx7R0e6jaxxdsqP9t7TKI7ranfa08ZEhrl11HxpSpHn8UOnKUNnKSSuTpL4/TUnSbHtSixG4ECVwITPOr0E9IEmav1+sjHuYTdIUMH0ZJQA3d8SfmlESIk+af3ce25FV4itP+vro6qvdR9fvnW1nSpMxSVKKCFmKGPe3Pin2FVBTxczQWJsn5n2vs9AJSl9/e311tAmZJHSN1XecY323Y/vG7XpfXxp5fH1iD4136uM49Fz7PH8hMeu4g8cO9Dk17uB7dOKkfPRzdjdNkPlk600ZU/N/qqEiYvS+to8PiB8aOyT+lNghfaT+rvP9P/jQWHdn6UtLC0ORKRkuPjTXV7kzF26gzZ2r27dnjjtIaIbLG7gQLa8AvkNVXwIgIr8G/BfgAcBvicjDVfVVvZGGeUl1+1AROWleqai6nNMDcXVXr5gYH+Bzq9u2OHkx7vrcDwN+sbXv83DG62VTr1AEbtJ27e7inb1EBotvNkp8CTIvXt/k/jz+hPVU+sYUWtYVKjUGjxt53Ecfh0a/fcKj3bdXVknnfWzIiq41K3qEiY8MaY6rvX1IjNTb9sXKvjSpY/at7eHi7YuPZrsuUQKHsuQ83pAwqX+HbkHS93tbnNS/h8qT0JKcoX0+ZTk+JTmxM1H6+unqb297ITLl2ERKyVzV9NkwhlESYwKjtLgp+z3TedkZue5zLMbGP/fxKaWPWHHqc7MjEy8JeAOuYklwF6s5ECMicsLFxW5elznuIKHC5TLwauDJqvorzR2q+h4R+QLg54FHA78mIn9bVf9r6KBU9S0i8kLclYi+EXhGY/dTcFkpz1TVDwKIyGXgeuCKqr6lbigi9wTerqrvacYXkbvgsnKoxtvkOcA/Bx4nIs9Q1VdWx9wC+O6qzY+G3qe9/lGuGchwmSspDvrzyYiYuRDs2JinrFEzFDOknKkvjq9c8nr8WuMZkh5dY/LNPum9LyMSpO/YvfKfAXnSlh9u37gYcfu75UjXse0MkrpNlyRp/t6XUXLx9/7j084saR93sNBpnzRptvEVJ3AoTJrt2rG7hMcUiTLU3mff+e+7/uOabWPKFIhX2mNCpZ/6/i653skShJ5kx5YrXqU+C6Szb7GcCIZLipKVjCQqYTmW0pgS1xWC9GUva4+/lj58+4kV58jlSoqrFKnq+6uFdT8H+ELgP3U0ux8XS4D4XLU4WdwxQoXL/09Vey/zXGV9PFZEfgD4ZuA/ici3qOoP9x0zwD8AXg48vRI5r8c9AA/GlRJ9R6PtJ1T7/wi4rrH9scA/FZGXAG/D1Wldj7ua0i2AFwD/qnUf3icifw8nXl4qIs8G3gM8Ame7noNbGHgygnJ551dSlEJkhMSfsk7LXMFR4yM6Lvo8HOeY9HDH+YuP7vb+YqVPhAzF8RUiMCxF+vb7yJHm9i5B0tzf7qtPlNR/n7bXU+mQJe1j+zJM2sd3ShMIEyftv7v2QX/2Sf13XwZKPa7m4zckPvqyUdp/jwkRH+kxJE5CZEpX7PaxQ9kpXcd19dHXdjDtO6FQCY0Vk7mL2sb4b+LSmFQJa9N77BFJlbUsCOwTd/C4DGvczIk7Fnur4rCE+Gvpw7efWHGOXKgMocCZ3zVjpvALODHyFSLyVFVtl/c8sbp9lar2lQYtGbeXIOEyJFta7b5VRG4EfgD4IS6ySUL6eouI3Ad4Kq6854twdVRPB57Szlrp4SU4SfKZuBKiWwH/B/gN4OeAn1M9nOmq6nNF5PNxUufRODnzZuBbgad3HRPCicK1Z2etbdNPzFMszhoiO9wYusfvIz3c8cOyIOy4sOySIREyFm9MiLTbdEkP1yZMjFy07RYkzX19kqTZpt33wXopI1klze1zhUlnjLaQaN6nA6HSIyjaf3dlmJz3ESBQmn37SpR2v11ZF3NESvP4vRgzMlO6xjYUv9kul1DpirNmoTJXpPhQumgxseI/jt5jC5xwrmnhW0gjVdaWTWJC5fjil9JHzDixZMqay8ZmkiLDpeKZuASOuwDPF5HHq+rrquVFngx8WdXuSc2Dqov7vK3682tU9Vkx4s5h8mWhx1DVp4vIH3NYshMS44+Br/FodyMdl3lS1V8Dfm1i37+JkzzREZRrBjJcQmUHDMsJX+lxEWua/Bg+PjzbZEyE+MT1ESJdcXzEyFC7zqvnNPofEiR9cfuySZrth0RJ828fWbK/fV+YtLednh0e13l/2hICDuVJV9ZJ3a5PnvT9fd7Xrl+wDJXxNNv7SJah8QyJlKG/u2KHypShbXt/77q3teO3j48pVNrtj6nsZwmh0qZEwWJSxX8cvccWOMneSumPCZWZcQst+SlddpQgOkymDLQp8Lt0ARRBEwkXVf2wiDwSV9bzWcBrReR9wK2BE1yCzZNU9YUlxB0imXABUNX/LCIPSdnHGhFVrrk6XlLUX2YSNgmYKkCG2saQIV1thqRIV/s+4eHaDguMmrHsEegv3fKVJO227WM6F5/tkSVuPNOFSbO/MWnSjhkkTpr72scMleaMtanbjf3tI1HqMfZlo3T93SdSfI6dvN6J5zExs1OabacIla6++trnylJZUqjkkCltSjshDDnxNqnScWyBE+2lS3+2spaKCZXq2JVmp6yhDMdkyvS+SvvuLIxdmqsUAaCqrxGRewHfDnwJbhmRdwO/A/ygqk5aYyVV3D6SChcAVf3t1H2sDVHlmrND4RK6WO5UERLSV6gQ6TpmSHa49n5ZIzCc/eMjSNrt+jNZ+rNJmsf1LlDbJV96skua+3yFSbPv9r6xbJO9Y8bESd2mPVlsyxPwkyUhAqWrzRyJ0tVmjkiZ2qYdf4pMacfai3dkQgXyS5USZEqbkk4Qc4kVkyrx466p9KekLBUTKtWxhQqVtWenlCBsYsVZq0g55pKidGu4AKCqfw58U/Xj0/5GOipf5sadQ3LhYhwiqlweyXCZKkRgmhSBcTHijvOXI+AvSMAvi2Soz7FskvaxsWVJX79jWSZd+3yyTfaOa/frm3XSPnZIjIQKlK5t7ce2S6K0xxiajdI1riGR0rVtbkbJ0JomfeJkTUKlfczWy35KFCpNSpArJWermFQJYwtZKiUJlVRxj0morDk75VhkylazUiwDZhAFztKt4bIZTLhkQIBrPUqKIJ4Y8TlubO0YX0HS1Xa49CdMlMA0WQLzhMnYOMakSbPNLHHSbDckT+rj2uOYIlDqfWPbuqSAj0Rpj91XpBzEiihOxspzQsSJr0wZ2hcqVIbG1NdX35iGYvTFWlOWSulCpaaEk7otZ6uYVJkX85ilylpi+uzvPa7QDJK1xl6qjzXJlBKzUkr43i0aQRNnuGwBEy4ZEFUuNSYQoVkjdYwxQgRJV3tfSdJ1bF+cEFHStW+KMGm2GSrN6dvfl23S1V+vOIE48gSmC5R2331ZKH3bQiRKva9LIMUQKSm2tfsKKeuZmp3S1187zphQ6Tu+r8+u9lvOUlmLUKnJfYJnUmVi7MKyI5aUKiZUyojps7/3uMKkYO7YW4gfK8baZMrSIuXkeMuJlKRXKdoMJlwy0FdS1CULYkoSmC5KYHh9mN4ynxFZ0m7jk+ESQ5r49DuadQLT5Unz2FgCpbl/TKLU2/vGGZqN0teua7xDpSixZUrffQkVJ6mFCuQv++mLVbpUWZtMaZNTruQQKyZV4sU81iyVkp6DxWNuKMtqLG7JsUuIHyuGyZR+fEVK7n+S5EThzDJcRjHhkoETVa69cuXib68SIX9R0o45tpDunOySruNTSROILE6gP+uk2b5rQjcn+8T3mOY+H7HS3D6WjdLu11ek1Nt97qdvac1U6dLc3u5zTnbKYNyChcpQjKFYpQsVWL9UgXwnZKVmq5hU8WMNUqWULBUTKhOPyyQ9LPMlbQwTKf2YSImGZbj4YcIlA6JwzZXuNVxCskq62g/t6xMm7bZThUnNlGwTnzHsxUopT5oxYguU5v45EqUe99i6GaEipb297z75Co0U65zEzk7Zi51QqPTF6Ou36xjLUimf0sWKSZXq2IIm4LGlypJZKqWU/ZT0fK5JqJSaQbLW2D7xY8RYk0wpsbzHrk4UFVvDZRwTLhkQVU6vno2X/3hkl3S19S0TmiNNYAFxAsPypN22L15Xxkh73xyB0mwTQ6J0bY8tUuoxjWVITMk2mSI+fKRGDqHSPCbFOipdx2wxS2VLQqUmh1g5xhKgrUxQ+6RKKZP6rZb9rEWolJhFYqImPH6MGEvIlK1mpZhIyYBYhosHJlwyIKpc28pw8RUmh23nSROYLk722vSNeY486YvrI0m69vvua+/PKVGa20NESmf7mRkoU7a3+55S1hNLqIz1M0eo9PXtOwafWCVLlS0KlZqSxYpJlXIm9mteoHbNZT9bFyqpJs5WRhQWO9bxc2XKFrNS1ixSrOTIlRRZhssoJlwyIKpcc/OFcEkpTdrHeV12eUl50owVU6B0jbFPlAzt68rsOI9fsEhZYntX/3OyW/riN9vMESqwTNnPUIyhWKEipE+qmFCZxtInTiVmq5hUGab00p8SslSWFCBbFyolSg+TKd1Yic8hS6+TkkOkHPHViYwwTLhkQBQu39y9hssi4gTmy5O++CESJKSMp2+cUyRKc99QSdJckTJ0TCkyZWxfc/+UGGMC5KCPBEJlbKxD4woZh088y1LJT6lixaTKxOMiT8aXKv1Zq1AZ6qv0eGP7BmMWJFSs1Ccs9tzj11Lis9WsFBMpxXNV7fEaw4RLBkSVy1fOgIDLMk/NOuk6fkr2CcwTKD772+1iSRQYXjTXV6Q0980VKSEx5kiTFNkpvvu6+skpVPr67ztuilSJlaUyJdYYxyJUapYUKyZVPOMWkjERU6oslaVSQtnPVoXKmsTH2uKmjj33+K2U+JhIGW8TW6JYOZERgAmXDIgql26+crgjljxpt9nbnkGgNNsOiZKu/T4lPV37hibuU0RK1z7frBSf+LGPaY9lqjRZQqg0j+sTKu3YlqXSEe/IhEqNiZX4sdcuVZYq/VlrlspS66iYUFkmZq64qWPPPT61TFlTVoqJlLh92oK65yi2hosPJlxyoMCVWkDMkCcwnEHR3D+nTagkCdk/Nxul2S5EpAztG3o8YpfVRFvbJCCO79g6+xkRaj4ipPQsFd/jakyqLMNSYsWkykjMQiaaa5QqJlRG4q38NZkjZolxfWLPPX4LMsVEyjAxRYrvYxBbpMQoRysdFc5MuIxiwiUHO4WbOjJcYsiRKW262s7dX6JIgWklPkP7UmaOzM1O8e1nr6/ChUrXsWNSY00L1B6zUKnZolgxqRJXquQu/cmdpWJCZfoYpsZLFXNrcecev4YSn1LKe0oTKTnWR8khUkJeo0eQCeMyXHKPonxMuORAFao1XAZLaJrMLeXpaxNLovTtnypSuo71Ke/p7XNBYRIsORYUKl77J66j4jO2oTH0HbelLBUTKhcsIVaOQar47O88poD1OCxLZeCYpWRT5sc59himxltTzFxx5x5rWSmOpbJS1ipSSs9GOQKJEopahssoJlxyoAo3Na5SFJKF0m4/RaI024yVMY1lPOxtmyhSmvvnZqWMxZoqdEIlh6/AKEWotI8rLUulK2asLJUpsYZIJVXmjDHXCUJJ2SqllwCZVBmn1CwVEypx+x/rq4THKFXMXHHnHlu6TDGR0hHnCMp6ckqUYygnwtZw8cWESw6aGS41IZLER8TEykZpth27UtIUeVCCTAmJ244xR5iELPyaQ6gMxeobR99xlqUyEC9yWVKbJWSLZavEiV3ChLbU0p+1ZamYUAnvf2q8Y485FnfusXMmjrlFSow+TKTM7y9HNkpOiXJkWTA7TLiMYcIlB8qFcJlb0gNli5Te4xNlfcTIGAkprylVqMA6slR8j6tZm1RJLVO6SPlFn1qsmFSJN5Y1rqcSK7NkbeuoLBZrpa+9tYgPkymt/RvISjlWkXJM2SgmUWahYIvmemDCJQeq8JHGorkxy3qa7cdESl+MOVkpYzFzy5R2uznCJKZQ8RlX+9ils1TGYk3pv4slpMqahUqTVCcAJYgVkyozxxAYb4nSn5xZKjnLfo5FqJQiKpaWKSnjTj225BIfEymtOIWJFJMoy/S9FVRsDRcPTLjkYFeVFE0VKTBvnZS+/VOyUppx5wqR2Nkic4VJqUKl3W5tWSqwzFV/YkiV3EKlSYoTg5RipZRsFZMqF1iWil8fWxEquV9vKeJtJeacY485K8VEyvS+lhYpJlGOht3OHp8xTLjkQIGbG2u4zBUpPm3mlPiUsL+rXYo+vfqNJFQgfZZK17FbKf3ZmlBpEvOEwbJVpsfNLVVylv5sIUtliXVUTKjEizUUb00x5xxbqkzJLVJi9LGUSNmyRNlCnyH9hnKEZUdWUuSHCZccNBfNDcmm6Gszp8THp+9smSILCpVme9/Mo61lqfgeW7MGqVKqUKlZi1hZKlvFpIofpUkVEyoecVb4OrNsl7jHpZIpx56VEuN7dKvZKCZR5hEyxmPNglG7SpEPJlxyoOoyXOZmpfRtjylTpvYxKjUS9evTt3ebiEJlKN7QePqO3Urpz9aFSpMl05Qnx14gW8WkyjglrqeydqliQiVtrCXjpYo557ipk60tZ6WYSGntX3FJzzFKlGMVKBOwNVzGMeGSAwU+cvXi71QyZWqMKGU3KxYq7eNjZKkMjanvWCv9WZdQqSldrJRQAmRSxbJUxuIXFyfjmjK5Y60h3ljMqcetscTHRIojhkjZcjaKSZRl+94oiliGiwcmXHKwU7j56uH2JWRKaIy97QFCZU6JVKlCJSTm2NjGYo3FLUmqHJtQqTGxYlJljDVIFRMqw9t742woO8VkSty4pWalmEgJ62dJsbHm7BffPkPIXcqTu/+VsZsxDTgWTLjkoC4papNDuOxtn7l+StT+PSXB3MVp223nZKl0HX/spT9rFipN5p6cbFWqQILygyOUKmvOUjGh4tF3odkpaygdmnrc2kp8UmalrEGkmEQpqz/fPkNYSxaKCZQgVOHMrlI0igmXHLSvUlSTUmjEKPeZ0//BGGYKlXaMXFkqY/GG4pYkVY5dqNQcq1jZolQpSagMjifgsSip7OfYhMqxZ6dsXaZYVsrE4zckUkyihJFTYpQgUI68lMgIw4RLDnaRMlz2thcgVA7GsVDZT7ttjiyVvrhrL/3ZmlCpKVGs5M5WMamSZjw5slRyPI4mVIbj5I61dLypx61JpphISdvHGrNRSpQ2IawhC6UEgWJZMHvYornjmHDJRZdwge0JlZB+lspS6Tp+K5dSNqmyz5wTm1zZKiZVPPteaenPWst+TKjEjbNUrBTxphxTWomPiZRu5oqULWajmERJ22+Kvkvo/4iwRXPHMeGSg50OC4TShcpBuxVkqfjE7Iu79tKfLQuVmtLESs4SIJMqicaysFQ5ZqFSmkyJGctkil/MVCVMqWRKTpFyTNkoJlGONwulBIES6xLiG0KxNVx8MOGSA014laKudnvbEwoViJel0tX+YH+m0h8wqZKLtYmV0rJVBuOtIMNgS1kqWxIqa3jtLBEnd6yxeFOOWYtMMZGSpo81ZaOYRFlPvyF9p+of0gqUVFnSpaF2WWgfTLjkwqekaK5QCWpXYJZKV4xjXU/lWIRKTUliZW3ZKiZV0kmVpct+Slncdg2vmxLjLBVr6jFTJjFrykpJWd5TskgxiZK2P198xmUCJS6lCJQjyoRRQI9sijIFEy452OEm3CmFSrvtWrJUfGIOxS1FqliWih+TF11cWbbK1qVKKeupHGOWSulCpTQJYjJlWrySslJMpPTsX0BwrK10yKcvX44tC2XLAiX0M+KIBMoULMNlHBMuWei5SlFN7LKfdpyQLJXQ2J37C1xPZUqsg+MtS8WbUsSKSZXpfS+9nkoJWSomVKb1Fxpjy3GGYk05ppQSn7WV95QqUpZYZHZrEsWyUNL2m6JvOI7sk2NcfFdhZ2u4jGLCJQduhSH3u69Q6fp7b1/GLJWuGMe6nsoxSxUoQ6xspQSo9EnysWWplCBUUq4/E9Ln2iXIlmTKFrJSShQppWejlFLSYxLFBEpsSpEnEPa4HcuaLS0UOLMMl1FMuORAG1cpSlH209U+V5ZKX+y1SpVjFyo1kyYZlq0S1M/cvrcsVZbMUsl9/ND2zraFZKeYTNmnBJliImX9ImVN66KsuZSn5MyXFH1DGnlRUumOyZNkqGW4jGLCJQfK+FWKIH+Wik/codhrXU/FpMoFucVKymwVkyqO3KU/c7NUcguRrQuVkkRIzPds8PtyoH1MmVJKVsoxiRSTKHH78eEYslByCpStZ5+UJE+OfH0XBXYTlq08Nky45KDOcFk6S6UrRglZKlNiHRxvWSqzKF2smFSZFyOFVCkxS2XuY5Xs+EIzclLGKDHOlPahE6FjzUpJVU56zCLFJMq8vmL2l6vP85iZs09MnnSyO8Y1W9qocGYZLqOYcMmB4mRLqFABkyrebU2qdJJSDMztO0UJ0LFIlbmlPymyVEyoJOrriDNchuKEti+5xMdEyvTYJlH8+4kxjjWX8ZhAiY/Jk6NDsUVzfTDhkgMduEpRytKfvvhrXE/FhMowWxQrS5QTLC1VllxPJWeWylKPU26hUoIM2WKGS2j73CU+ayjvSSFSSs1GMYkSNo41Zrzk7NPkydHJk92RlxG1UVs0dxQTLrk41kVqLUslLrnEikkV/9hrKf3JmaWSU8iUWu5TSnbK2mVKyVkpxy5S1p6NsoSo8WFsHFsv49mSQNm6PClMnLg+yhvTqlDY2XRtFBMuOYlZ+hNyPFjpz5ooNVulhBKgY5Mqa8hSMaGS7/gcMYbihLaPJVNKzkoxkTJMbslRgkTZchbKFgRK7uwTkyc98csaj2E0MeGSg512X6WoiUmV4+NYslViTYZKlSo5S39yCZW5x5cmVHKvFdMXo6RyodD2JcqUErJSShIpucp6TKKYQAkhl0AxeQIcrzg5i1EWuEFU4cpVe2zGMOGSk60sUmtCJYwSxUqpJUBrkiprKP3JJkUyyaagfjaQnZJDpuQs8TGRMj+mSZTpLCFRti5QTJ5EHkM5sqJEcbKUNLHsGaONCZccKP2L5nZhUmWdpCplmNPnFLFS2roqS2UZ5Cr9MaHSc2zGsqQlj08dI6RtrqyUmJ85pYuU0sp6UoqO0iXKUrJhyyVDWxEoK5Mnx5Z1UrI40SNbUFcRu0qRByZcSsEWqV0vpYmV3CVAuaXKGtZTmSpVTKikESJbyG4JbVuaTMm9TsqUE3jLRlm/RDGBMhIr4uRxDdknG5cnJk78OTZxMge16eMoJlxyoOwLFstSWQdLixWTKv0xl5AqJWepHJtQWUt2ypZkSolZKSZSWmNZoURZQxbKVgVKjuwTkydJhnCM4qR0aXK0ZUQKZ3ZZ6FFMuGRBw+WHSZVl2EK2SqyJz1qkSilZKqWIh2MTKmspNRqKEdI2KNW9sKyUrYmUNZX0rFmirGW9FZ9+Yva1heyTnPKkAHHiYpcxDihTnJQuTY5WtFQoWEmRByZcSsKkynJYtspIDJMqxZb9lCxUShMiJZQK+bbNUeKTWqRMiXWMImWNpUJjpBYcW1xvJdbE0uTJICVknaxdnJScbWLSZHl2Ni0dxYRLDpRxuWJSJQ5LipWxvmJmq5hUGemzgCyVNQmV0rNTTKasPyslpkiJXdZjEmWcrWShLCVQls4+OUJ5YuJkPqVmm6xBmoSWdW0WBc2c4SIifxP4JuB+wG2BPwWeD3yvqr5jYsyXAp8/0uxHVPUf+sQz4ZILEypxiV32MLWvXFKlL07pUmVrWSpLH9e37aBN4VKkNBkT0nbpEh8TKX6xoCyRcowSpQSBYtkn+eRJZnFRyjonpUkTWEaclC5NpgoTy4bZJ3dJkYh8B/Dd1Z874APAXYEnAH9bRB6iqn8wo4v3AR8e2OdF0cJFRD4ReCrwMOBjgLcDzwWeoqp/6XH8xwBfCnwx8NeBTwBuBn4f+BngZ1T311YWkeuAtw2E/SVVfVzofTEisZRYMaky7diYpT8lZqksLmIiv96jip7Cj+07PqTtXJmSIyvFRErPGEyimEBpxllQoByRPNly1klp4sSkiUmTIpiwLGksROSLuJAt/xrnB94vIvcEfh74DOB5InIPVb1pYjffpKrPmjvWYoWLiFwPvBz4WOB5wBuA++JShh4mIg9U1XePhHks8KM4UfMS4H8DHwd8GfCTwN8Skceqald9z2twcqfNHEtm+HDs2SpblSolZqnEKvspQajkEBux+4xxvG+7JWVKyqyUrYgUkyjT+ixdoqxJoCydfXKE8mTr4sSkSWg/6xAmZ2LlQ30okjPD5Xur2+eq6hPrjar6WhF5OPB6XLbL1wHPyDC+c4oVLsC/xcmWJ6jq+YMkIj8AfAvwPcA3jMT4Q+ARwK80M1lE5EnA7wCPxsmXX+449tWqesOcO2CMcMzZKrmkSqnrqUzJUilxHZUShcoas1OWkinJ3ssZyntyipTSs1GWFjNjpJQQWxAoa8w+ySFPMsoLEyf7pBYnW5MmaxImlg0zgoKeLf8YVVksn179+S/a+1X1T0TkF4G/B3wFJlwOEZG7Ag8FbgR+pLX7u3Cm6vEi8m2q+sG+OKr64p7tfy4iP4aTNg+iW7gYsVijWFmLVClhPZXSslRKEyqrWg+mcJmytqyUHCIl5hV7Ss5GWZNEMYEyP0aJpTsZ5EnOrJOti5NjlSZryDJZmzA5JkmjZCspenB1+17gt3va/CpOuNxXRG6tqh9YZGQdFClcgIdUty9sr7FS1Wb9Jk7I3B940cQ+rlS3V3v231lEvh63dsy7gd9S1d+b2NdxEHPyOaWPWNkqS14BqESpUnqWyhqFSgnZKSZT4h2fY52UtZX1rF2i5MpCMYEStx84GnmyVXFSUrbJlqTJGoRJLnFxTMIkBplKiu5R3b6+7QoavK66FeBTgVdO6OeJIvK9wB1wcuf3gOfg1oH9iG+QUoXLp1S3f9iz/0044XJ3JggXEbkE/J3qz//e0+wLq5/mcS8FvkpV/3dP3JFrPTs+22+YZWLZKuNxp/STez2VVFkqqe77VoXKGtdc8Wm3VIlPqqyUNYiUUiXKkvHGWGMWSmqBYvJkpL/xcZs48aOUbJPQx63U0hwTJuX0CctdMrs4FHZ5MlzuVN3+2UCb5r479bYa5p7ATcAHcdLlIdXP3xeRL+lzAm1KFS63q27f27O/3n77ifG/H7gX8AJV/dXWvg8BT8MtmPvWatu9gRtw6UsvEpHPGCpl2gRLiBWTKumkSklZKlsUKiVmtaxRpuTOSjkmkbJUSc8WJIoJlPR9wLLy5MiyTkyazGs/hZKzTNYiTNYoSywbBiQww0XhDiLSzDb5cVX98cBub1Xd9l2yGdycvubWgfFfCvwU8D+Ad6iqisjHAn8Xt7zJXwdeICKfpao3jwUrVbiMUT+zXhkleweKPAH4NtxVjx7f3q+q7wT+WWvzy0TkocBvAPcDvhb4Nx3Her3i7uOZCbMIOcuATKpcEKP0Z+1lP7mESu7slBwlQr7tlpApJZT3bFGkrFWi5MhC2bJAWSr7pDB5YuJkmBLEyRakiQmTvP2ByZKV8i5Vvc/MGD4uYPJ8u+vCOZUj+D4R+T3g+bjsl68GRmVRqcKlzmC5Xc/+27baeSEi34gTJa8DvkBV3+N7rKpeFZGfxAmXz6NDuBRNarGSOlvFpEp/32NjSVH2U4pQKb1kaepxqWXKGrJS1ihSTKKE91VSZstY3LmxIb1AMXkyEms74qQEaRI6jtQT3C0JE5Mlw+SUJVMXGt4ionD5SthzMZoOAojI/wT+aseuf6Wq/wqoF8D9qIEwt2r8Hm3BXFX9FRF5Gc4HPJwVC5c3Vrd379l/t+q2b42XA0Tkm4EfBP4AJ1veOWFcf1Hd3mqwVS5ylgEtna1SolRZqvRnibKfFJIgxn2NMY5Yx+Q4zqfNWrNSlhQpa89GWWqh2iHWIlFSChSTJ62+lpMnxyhOTJr0tU8/8S5dmJgs8e133ntoJ5YRsyB3BD6uY3tdGlSvz3LngRjNfW+PMagGv40TLnf1aVyqcHlJdftQETlprj4sIrcBHoir2XqFTzAR+Se4dVteDXyhqr5r4rjuX92+dbBVataSrbI2qbLUeipryVI5dqFiMiX82C2IlGOVKCZQyhcoS8mTArNOShcnJWSblCJNTJiEs7SAmPoaXGtWiYmShKhwGpjh4hVW9bqRJvUViD6t7Qoa1FcyUuD1scZW4VPSdE6RwkVV3yIiL8RdiegbgWc0dj8Fl2HyzHrhWhG5DFwPXFHVtzRjiciTgacCrwIeOlZGJCL3A363vQCOiDwE+Jbqz5+fet+CyCVWliwBWoNUWaIUJkeWSglCJUa5zxrKinzaeE2mTKRMOj50DKklSuxYU/ooId5YzDlxYZ7gMHkSzDGJE5Mm7fZpJ7QlCxOTJWP9rluU2DovxVInZ9wO+Bxcxkmbh1a3v53gYjf3rW5v9GlcpHCp+AfAy4Gni8gX4MzU/XBXCvpD4DsabT+h2v9HwHX1RhH5KpxsOQN+HXiCHL55b1TVZzX+/ufAPatLQP9Jte3euEtAATxZVV8+7661iJEFEBo797oqa5MqS5T+HINQKTk75Rhlytx1Uua+j+eKlLVno8SUHrGFR2kCJWX2yUbkyZrLdWKKk9zZJiVIk7ULE5Ml++S5vPJ6RUmMx8vWafFDFE4Dr1IUA1V9nYi8Bvh04B8Dj9kbl8idgb9d/fnvQ2KLiKhqb+aKiDwMV04E8Cs+MYsVLlWWy31wwuRhwBfh6q+eDjzFc8HbT6puT4Fv7mnza8CzGn//HPClOFv2t4DLwDuA/wD8sKr+etAd6WOuwPCJB+soASpVqmxdqKwhqybnMb5tUsqUXFkpS4mUNWSjpJYoa89CySFQcmefzJUnG8062VK2ydqkiQmT9Ex5nZkoCex/5uNViiQ5KyAzZ0lOuop5luFJOOHxaBH5F8DTVPX9InIP3Hz+NrhlQH6ifaCI3IC7vHPXVYb/qYjcDfhF4BWq+v7qmDsCXwPcULV7I/DTPgMtVrgAqOof4+7YWLsbuailam6/gYsHxbfPn8JddzsdwrQTthKyVUqSKkusp5JTqqQWKiWU+5QsU6aulzL1vVS6SCmhrGftEsUESjc5s09WIk+2Lk5ySpMShEnKcUCZwmQpGXEMWSU5RYlJkgtKuS9LIgonZ3lef6r6gmrpkKfhsly+VUQ+yMXVjN8FPFJVbwoMfS3OP3wNoCLyPtxaLbdvtPl94BG+sYsWLkdLqmyVLUiVLQmV0PaphUoJcqQkmbJ0VspaRcrS2ShbliillAXBdNGxYXlyzOIkV7bJmqTJMQkTkyXt/o5LlJQgFkyQlINkKCmqUdXvFpFX4CpZ7sdFVsvzge9V1XdMCPsfcY7kAbg1Yj8GV/HydtwFeJ4D/PsQkWPCJRcmVS5Yk1QxoRKv/dRjxtpMkSlLZ6WUJFLmZqOUIlFylvIsVRa0peyTI5AnaxUnW5ImJkzyxId1yJK1iZK1Z5NsTZLokZUOdSEKp2d5x6Cq/x/w/wUecwM9VTCq+lrgO2cPrIEJlxyILCNWUkmVKeNJKVVSZqlsRaisIZvFp00qmbJ0VkoukbJkSU9JEsUESjWOFcoTEycH5Mg2OXZpEiJMjlWWmCgZ6HfF2SRzJUnu8TcxQZIC4SRjhstaMOFSCjGzVdYgVVJmqeQSFTGFSunZKTlkSizxeMwiZa4E2aJEWatASbVw7IrlyVLlOkuKky1IExMmy8bfoizJIUrWKkm2lEVSmiCxS0R3oHCSOcNlDZhwyYFwccJeklTZSpbK1oVK6TKla3+oTDk2kVJiNkoqibJ1gVJS9kkmeXJM4sSkSdp+a0oRJscsS7YuStYsSUoQJFuWI7vAksBjQsi7hstaMOGSC58J1jFLldKFSinlPrllylIlPlNkSkqRkisbxSTKsnEgfvZJafIkc9bJMYqTY5QmJQiT0mSJiZJGfyvMJpkjSUyQ7BPrvWBiJAMFrOGyBky45ECkNWmekSnRdXwppT+xMkBiCYhUQqUUQbIGmWIiZZ4IyS1RTKDEiQfDYy9QnqQWJyZNfGKZMElFibJky6Ikh3BYexZJKYJk63LEyobCEOBkl3sU5WPCJQdC+olqKqmSO0tlCaGyWAZNwiyZsf0llPhMKe+ZmvWVuqxnDRIlhrTYgkDZgDwxcRJHnJQqTXIKE5MlcQidnC8pSo5Fkpgg2bYciVsylP+5MraNCZdceJUUBUiVoEyISFkqxyZUSrm60dj+2DKllKyUpUXKMUuUXAIlZvbJkcuTNYiTpaTJFrJMcgqTY5IlWxMlJkn82YogKU2OHJMYOSugVGxxFE6vlP28lIAJlxw0S4pySJUUWSqlCpXSM1nWJlNKFimpZUhsAdPHXAFSUiZL7uyT2PJkhVknc8XJEtLEp5+1Z5lsUZiEPPfHKkrWIEnWKEhMjsTnWMRICilS8v01ysCESy7qk/0lSn9KERdrESolypRjESlLZ6OsQaJsUaBsVJ4cuzhZSprkyDLJJUy2LktMlExjqiQ5VkGyJTlyDGIkthQp9X5uAVG4fLM9vmOYcMnBicC1p937lsxSmSxKEgqVkiTJGmRKjnVSpiw0G0ukTJUhsSVKiiyUpQWKyZNBUpXrzBEnxyZNlhYma8kuWaMsWWLCY5IkHjkFyewroxUiRiCW6ClzMnusUmQt4zTKwoRLLs5LigrMUpkrVLJmzSwsU0JKfHJnpSwhUpbMRilJoswVIGsWKAvJExMn8eJHmQQcgTDJKUtMlAy1L1uSHJMg2Yoc2aoYOTYpUvr4tojYGi5emHDJgeAmWL6T1a0LlZJKfXxlypJZKUuLlCnZKMciUUoVKCXKk8iiozRxYtIkjKWFyRZlSUmipFRJspYskjUKkhLkyBbFSEwpUtp9a1P6+JrEWp/MMMCESx5E4JpWSZGPBIkuMBIJleSlSgXJlJC2pYmUVEIk5noqPnFMoPS0TS9PYmedTI2XKtukdGliwsSfnLLERMkwa8giySFI1ixHtiZGjkWKlDy2JqlkyFruf1GocHpmj9sYJlxyIPSXC0UVL4FCZYoAyZnF0rfdV6akykopXaRMESIlSpSc66iUlH2SUZ7EzjopUZzkliZrFCYxpUXu9UpKkCVbkCRLChKTIwF9b0iMxJIipdyfLkoeW43JkONCFE7Oco+ifEy45KAzw2VhoRIzi2VtMiVVVkppIqUkiVJ6CdBcgZJDnkSUHaWIkzVKk6WyTMaEyZpliYmS9EKi5CySpQXJGuXI/LVSypisblmKlDimNilkyBrud5vkmYQrfEzmcLLLPYLyMeGSA+FQuNSkFiq5pcvQ9t5J6ETpkUOk5JYoU44pNQtlCYGSsnQngjyJmXWydnGSU5ocozDJJUtyi5I1S5ItCpK1yZEtiJGtSpHSxtPERIhJkNWiIFZSNIoJlxyIuMlkLqFSqkxJkZWyFZFSskQpXaCsVJ7kFidbkiZrESZrliUmShwlSpKlBMnkNZ1WmDWSe/IWQ4rkvg9tShtPzbHLkJRjXdPj0EXutZJKQIBTKykaxYRLDroyXHKKEt81Xbq2xy7xKVmkrFGilJi9slT2SSJ5shVxkkKalJxlshZhUrIsiX1yXIIkMUESeMzKskZyTui2JkVKGktNbBlS4n3sIlkZ5Erufx9LfD6t/TGKjq3h4oUJlxx0reEC+TJY+rbPlSm+WSmxynt8RcqcbJSlJcoUqbEmgXKk8iRUnMTONskhTVIKkzXJkmMSJccgSUyONPqbnOGyXilSyuSrlHE0iSlDSrx/bUyC7GPy4zgQ4MRKikYx4ZKDkwWFS9/2ueulLJWVErLQbIhICREjqSVKaZkrPidJOeVJBOlRmjiJnW0S+3LNc+NCWmGyNlmSQ5TkziYJkSRrFySly5G1iZEtSJESxtDkmGRIkhLGwu9zF6k/Y9b4mAxxlmAx902iILZo7igmXHLQVVIEy8qUmFkpS4qUrUiUrQuUGfJkaXGSu0xnSWmyVWGylCwpWZTklCRrFiQly5E1iZE5UqSEiVoJY6iJJUNKuk9tTIKk/RxZ22PRx5LSw9ZjmYZga7j4YMIlBz4lRX3bY8mU3CJlajZKyvKfNqklyhSBcmTyJKU4KVWarE2YpJYlS2aVxDpJzSVJSsgiSSlISpUjaxEja5YiufuH7YuQ6KWHhd7PLkx+DLOU+DDpsULUSop8MOGSA+Fisl2aTFlKpEzNRpkjUY5NoGSSJ2sVJ6VLkxKFyRKypDRRkkuSbF2QmBhxrFGKlDCh3KoMOVYJkurzYC33vwuTHvNY8nL2htHGhEsORODyaXkiJXZZT6ryn6HjcgoXCL98tM8xfX37yJPI4iTk+BBxsiZpUpIwKVmWLCVKcmST5JQkqU4a1y5H1iBFjlWIxJAhue9Dm6hlg4XdtzYmP/ZZQnxsTXock+zYcTz3FUAULt2cexTlY8IlB4IrKRqTKaWIFF85kkKixJQiSwiUFcmTpcRJLmmylDA5RlmylCiJJSxyCZJjkCOlipE1SZGcE8+5MqSkSfOxSJAU7+mS728fKcWHCY/1kUt0rPG9YyyPCZcciMAtLl/83TXZniNSYmajLC1R5mShpCjfGTsmkjzZmjgpUZqUIkyGHsfSRcnWJUmKk9JS5EiJYmSKFDkmIbIFGRKt3K+A+9LFscsPkx7jbFl2mOQwXIaLPR9jmHDJwYkcLpobS6RMKemZIlGmZpYsKVBiZJ8kkidLiJMSpEmpwqQ0WZJblCwpSUoWJCXIkdLESOlSJMeJt4mQeDFiE/t9WeJ97CKV+Fi79Niq7MghOtbyXojN2t8DRhmYcMlBvYZLTSyRMiVjpUmKLJScAiVUnkyUIO0JZEnipCRpkluYpJAlWxAlY5JkC4IkyX+hCxIjJUuRpU/S58iQ3BOKLUqQmO+90u5bG5Meh2xJeCwtOUp/vacg92v9GB/z2exsDRcfTLjkQHAZLqVKlFiyxXftmLG2Xf0EypOUWSc+4iSnNClNmKxBlsz50k0tSZbMIol5spxLjoRlrZQjRbYoRNYoQ2ZL04JO4I9FfqQQH7knglPYiuxYUnSU/LqOSe7X87E8zoZRY8IlByJwbfXQp5IoOQVKZnmyJnGypDTpEiY5ZcmaRElqSVKaIPESlBmzRlKdrJUoRZY6MZ0qQ9a4pkopJ/sx3kOl3JcuYouP3JPEUNYuPJYSHSW/hmOQ83W79cc2BN914YwwRMXWcPHAhEsODkqKImah+AqUWNknC8uTVOIklzQpXZjElCUpREnKbJLUkmTNciSnGClJipQsQ9YmQnJPDLYqP45VeqxVdiwhOUp8ncYix+tzy4+nLyXJDHs+jBIx4ZIDwQmXHAIlkTzJIU6mZptMlSa5hUmJsiSFKEklSUoQJEvLkVxiJESKrF2IlC5D1ipB5r4PSjvpjik+Spcea5QdqUVHaa/HGCz9OtziY+hDKTLjWB//Kewo+zM6JqJwcpZ7FOVjwiUHJ3JxWehQgTI1+2RIniTOOgkVJ6VKkyWFSSxZElOUpJAkKbNIlhAksU44c4iREqRIyhO4kkXIZFGZbR2TbciPY5EeaxIeKUVHKa+7uZjYSEtuoXFsj/cUShYYa/q8TYUJl3FMuORABC5VJUVTsk8iyZPc4iSWNFlCmCwtS5YQJSkkSU5BsoQc8ctQWV6KrE2IhMqQkkVIntKd9S0+2ySG+ChVeKzh5NskRz9Lva7W/jj5klNmHMtjHEqp8mINn53GIS7DxZ67MUy45GBv0Vw/eRJLnKSWJkmvBrSAMOm6vzGySkK/+GNnk6SQJHO+HFPLkSXFSC4pciwyZA0SZOrrNfeEYK74KE16lH7CnkJ05H4NzWGJ18+aH58xcsmMLT+moZQoL0r/HFwD9hoPwzJcxjHhkoMTYXdt90O/85QsMcXJlEyTSWuyTBQmS8mSlKIkpiRZWpDkliNLipGYX7IpvrBDZEhpa6Msl6WyLvmxJelR6ol+bNGxxpPx1K+TNT4mYywtNLb4GIZQkrwo9bNsDRz76/josDVcvDDhkgEFrlYlRSHiJCTbJMWlk9vCJGZ2yRxZEqP8pls6hX35x84imfKFn1KOLCFGxqRIyULE+0pXBYmQZS5rXO5aKk3miI8SpEdpE4RjlRwpXwtreQx8WFJmbOlx86UUeVHa51KpHONrdCmWurz5sWKL5vphwiUDeiLcfO3lzn1zpEkKYZJDlvh+8cTIJomZRRJbkEz+T31iMbKUFIl5ApJThgSJv+TlOWXLj7VKj5ImFTFPLkufBKR6zku/32MsJTPW/jj5kltelPT5UiLH8jpMzZrFRAn/9DhmTLiMY8IlAyrC1UvuwyFo/ZUEwmSqLIktSpaWJCXIkZRiZAkpEuskx+uqVBnXP0mbkVLWeik1U8VHrpOeEiYkMU5WS544pHhuS76/Q6QWGmt9XHzJJTBK+Jwoia2/zlKxRjFhQsJIhS2a64cJlwzsRLjp8mWPDJYwWZJSlKSQJCkFyVJyJJUUmXsiFONEKkdWiLe0S3ZVnjLWSKmZIj6WPrHKOYnZquSI/RyWeB/7SCkz1vQ4+JJDXhy7uNji6ygVa5ETJiTysZbXyBTss8KoMeGSiSvX7D/0ky6HnFiUzJEkMQRJCWIklRSZ+yG8ZFZILglSivwoXXrkmPzMPUEq5SQk5vNUyn0awmTGMEvKi2OUFlt4jaSg9AmnyYjlKP21EIq9548AhUs35x5E+ZhwycDuRLj58vBDf3i55fEvvKUESQw5ElOMpJAic74kRhc3XrCcJ272Sd5FYUPFxxIniUtPmuacjOU+8Yn1fOS+H0OkEBol398hlpIXxyAu1voaiE2pk1ETEvEp9bkOwd632+BsA69FIz8mXDLg1nCprlI0UZL4nmTOlSOxxEifFFlaiKSWIWPHx8s6ySc/QsRHyhPRpSZaa5QcMR730k4WY8uM0u7fEKnlxValxZqe49iUNGE1ITGPkp7LEI75/bc2jlUq2Gt0PmIZLl6YcMmAItx86fChTyFHQj5MYkqRKccMyZCU5TtxMk6WXfi1BOmRepI25SQzx5fnrEttF/JlH1NmlHKfhkglMLYiLtbwHMYi92TWZIQ/uZ+rEI7pPbQGjkko2GvPMMrDhEsGVDgoKfL9gIwlRULbp5AhQ8fNzzRZ5tLFvuIj9kl1yold6Entkl/uky+VnfkEJIbQyH0f+kghL9YqLkp9jmKQa7JrQqKb0uXDlt8LpbJVqWCvJQPcP6uN8pAdXLop9yjKx4RLBlSEmzoyXGpiyZDYmSnzskzSXZEH/MRHzBP3FBPCkBPoJU5AJi1anOHEaK7MKOlkLqa8WJu0KOl5mMPSE+FjFxIlioetvJZLY0tCwV4j28bkwDDH/r1lHB8mXDKgIlwZEC5d9H05T5Mq6eTHmPiI8SEbeyLpe8JewqWIL45Z5st8jszIfUIZS16ULi5yP85TWWqifEwndqXIh7W+JktgrVLBnvN1YmLggmP6rjCMWLg1XOxzZAwTLhlQ4GojKyW2AEktPWJMQHNJjuCrKSU+iZwiNLItxDpTYJQmLtYyQUg5id7yCWa+Eph1vK5ysRahYM9jeRybHNjy57ORl1LkvGEcCyZcMrAT4cOXr5l07NxJq8+H7BaupAPhMmPRtUhmyIvc4qLUiUiKE4gtnPAuX+JS5utjSUqVCvbcLMOWxcAWPhONdNhE3jgW7LV+wclZ7hGUjwmXDKgIZ62TltSLxMboo02I0Eh5oj9FXuSQFqVMdmJ+Sazt5Dv1F2Qpz3FqShEKx/J4p2QLcmBtn0NGODa5MUrFXpvrxZ67+YjmEy4icjfgbwD3AT4HuDdwDfDbqnr/SH38TeCbgPsBtwX+FHg+8L2q+g7fOCZcMqAIN58OP/S+MiPFhCNEYCwlLrJc7jfCB3GpE4FUXzJbmgDnkgpbegxjsyY5UOp739jHTriNWNhrKT/2HBjGwmQULsC/BB6ZKriIfAfw3dWfO+ADwF2BJwB/W0Qeoqp/4BOraOEiIp8IPBV4GPAxwNuB5wJPUdW/TBlHRB4AfCdwf+AWwJuBnwaeoaqzXloqwk0nlw62pWCpydvUL7kSJiVxMz7W9WW/hFRY22MSixLlQAnvt2PCTv6PF3vu42GPpWGUz9wrRhrrJaNwOQNeD/xP4JW4LJfHxwgsIl/EhWz51zhn8H4RuSfw88BnAM8TkXuo6uiFsYsVLiJyPfBy4GOB5wFvAO6LS+t5mIg8UFXfnSKOiDwS+GXgI8AvAe8BHg78IPBA4LFz7psCV05OvdouPUGae2JT2uQ6lVAo7X7OJaccMAkQjk1A1oE9T/3YY2MY+9iE1ZiKfZ4auchZUgR8eTMJQkRuiBj7e6vb56rqE+uNqvpaEXk4TvTcFfg64BljwYoVLsC/xUmSJ6jq+R0RkR8AvgX4HuAbYscRkdsCP4GzZg9S1VdW258MvBh4jIg8TlWfPfWO7US4+STeQx9LKqxFIiwhB45VAtiXdjqO4bE9hvtolIlNVreHfZ4YhmEUjsLJ1Uxdz6w46aPKYvn06s9/0dHvn4jILwJ/D/gK1ipcROSuwEOBG4Efae3+LpxNeryIfJuqfjBynMcAdwR+tpYtAKr6ERH5TuBFwN8HJgsXRbhZ/DJcmqxdAtjJk2Ntj8PaxmtMwyas6bH3kmEYa2DOlRQNwzgehE1epejB1e17gd/uafOrOOFyXxG5tap+YChgkcIFeEh1+0JV3TV3VPVTv4kTKffHCZCYcepj/ntHvJcBHwIeICLX+tRsdaHA1Uq45DoBtxP/+BzrhNVeS4YxDZvUGMdOiWtdGYZhGJ7kLSlKxT2q29e3/UGD11W3Anwqbg2ZXkoVLp9S3f5hz/434UTJ3RkWLlPi9B6jqldF5G3APXF1W69v7hcRHRjLOZ/w2dfzYSn1od8uNrlZF3YibhiGYRiGYQxh//jLywaFy52q2z8baNPcd6feVhWlzvpvV92+t2d/vf32CeLE6ruXP33VW3iSPOpVU483DGMSn13d2nvPMJbH3n+GkQd77xlGHj6bi/ffJnk7r/rVG5A7BB52CxFpZoT8uKr+eMxxzeRW1e2HB9p8qPH7rccClipcxqhVpldGSeQ4vceo6qhirbNgVPU+AX0ahjETe+8ZRj7s/WcYebD3nmHkwbfyYc2o6sNyjyEBPn4g6LkttcaiziK5Xc/+27baxYwTq2/DMAzDMAzDMAzDMDwRkf8pIn/e8fPE8aNnUy+A+1EDbW7V+H1wwVwoV7i8sbq9e8/+u1W3fWuzzInTe4yIXAI+CbgKvHWkb8MwDMMwDMMwDMMw/Lkj8HEdP6PlOxGo12e580Cb5r63jwUsVbi8pLp9qMj+tZBF5DbAA3F1Va9IEOfF1W1XitTn4WzXy6deocgwDMMwDMMwDMMwjENU9TpVlY6fGxbovr4C0ae1/UGD+kpGSusiOl0UKVxU9S3AC4HrgG9s7X4KLo3nZ1X1gwAicllEPlVErp8Tp+I5wLuAx4nIeb2riNwC+O7qzx+dfOcMwzAMwzAMwzAMwyiNOmHjdsDn9LR5aHX72y2P0ImolrmeTyVPXg58LPA8nD26H/BgXAnQA1T13VXb64C3AX+kqtdNjdM45lE48fIR4NnAe4BH4C4Z/Rzgy3XiA9dYvMyuYWYYC2LvPcPIh73/DCMP9t4zjDzYe29ZROQG4LtwEuT+M2O9Gvh04JdV9TGtfXcG3gDcBvhHqvrDY/GKzHCB8+yU+wDPwgmSbwOuB54OfG5bksSMo6rPBT4feBnwaOAfAVeAbwUeN1W2GIZhGIZhGIZhGIYxHRG5VkTuUP9wscjtpeZ2ETm4EI6I3CAiOnAlqSdVt48WkX9RLUWCiNwD+K842fJW4Ce8xmruYFnMdhpGHuy9Zxj5sPefYeTB3nuGkQd776VFRL4a+BmPpr+mqg9qHXsDLhum9/kRke8Enlb9eQZ8kIurFb8LeLCq/oHPWIvNcDEMwzAMwzAMwzAMw1gSVf1u4AuBXwH+ErgWl9XydOBevrIFLMPFMAzDMAzDMAzDMAwjOpbhYhiGYRiGYRiGYRiGERkTLoZhGIZhGIZhGIZhGJEx4WIYhmEYhmEYhmEYhhEZEy6GYRiGYRiGYRiGYRiRMeHiiYh8ooj8tIj8mYjcJCI3isgPichHp44jIg8QkReIyHtE5EMi8nsi8s0icjr/nhlG+cx9/4nIx4jI14rIfxaRN4vIh0XkvSLyGyLyd0Xk4LNQRK4TER34eXb8e2oYZRHju686pu999OcDx9l3n3G0RPje++qR7zAVkbPWMfa9Zxw1IvIYEXmGiPy6iLyvet3//MRYNuczALtKkRcicj3wcuBjgecBbwDuCzwYeCPwQFV9d4o4IvJI4JeBjwC/BLwHeDjwKcBzVPWxEe6iYRRLjPefiHwD8KPA24GXAP8b+Djgy4Db4d5jj9XGB6KIXAe8DXgN8NyOsH+gqs+ZcdcMo2gifvfdCNwe+KGO3R9Q1X/VcYx99xlHS6Tvvc8AHtWz+28ADwF+RVW/pHHMddj3nnHEiMirgU8HPgD8CfCpwL9X1a8MjGNzPuMCVbWfkR/gVwEF/lFr+w9U238sRRzgtsA7gZuA+zS23wL3JlbgcbkfH/uxn5Q/Md5/uBPLhwMnre0fj5MvCjy6te+6avuzcj8G9mM/OX4ifvfdCNwY0K9999nPUf/Eeu8NxP+tKs4jWtvte89+jvoHJ0TuBgjwoOr98PMT4ticz37OfyzDZQQRuSvwFtwJ4/Wqumvsuw3uP+YCfKyqfjBmHBH5f4GfAn5WVb+qFe8hwIuAl6nq58+/p4ZRHrHefyN9PAn4HuCHVfUfNbZfh/tP379T1a+eeBcMY5XEfO9VGS6o6nWefdt3n3G0pP7eE5F7Ab8P/ClwF1U9a+y7DvveMwwARORBuKzooAwXm/MZbWwNl3EeUt2+sPmGAVDV9wO/CXwUcP8Ecepj/ntHvJcBHwIeICLXjt0Jw1gpsd5/Q1ypbq/27L+ziHy9iDypur33jL4MYy3Efu9dKyJfWb2PvklEHjxQk27ffcYxk/p77+ur259qypYW9r1nGNOxOZ+xhwmXcT6luv3Dnv1vqm7vniBO7zGqehX3X4hLwF1H+jaMtRLr/deJiFwC/k71Z9eXHMAXAj+Gy4L5MeA1IvISEflrU/o0jJUQ+7338cDP4d5HPwS8GHiTiHT9t86++4xjJtn3nojcEvhKYAf85EBT+94zjOnYnM/Yw4TLOLerbt/bs7/efvsEcWL1bRhrJfV74PuBewEvUNVfbe37EPA04LOBj65+Ph+XXvog4EUicquJ/RpG6cR87/0M8AU46XIr4K8Dz8StF/HfROTTE/ZtGGsj5ev/y6vj/puq/nHHfvveM4z52JzP2MOEy3ykup27GM6UOLH6Noy1Mvk9ICJPAL4Nt3L849v7VfWdqvrPVPV/qer/qX5eBjwU+G3gk4GvnT50w1g13u89VX2Kqr5YVd+hqh9S1T9Q1W/ALR54S+CGVH0bxgaZ8/r/uur2mV077XvPMBbB5nxHhgmXcWqjeLue/bdttYsZJ1bfhrFWkrwHROQbgX8DvA54sKq+x/fYKrWzTsX+vJB+DWNFLPH982PVbft9ZN99xjGT6nvvHsADcJe6fUHIsfa9ZxhB2JzP2MOEyzhvrG77amXvVt321enNidN7TLX2xCfhFvp860jfhrFWYr3/zhGRbwZ+GPgDnGz58wnj+ovq1lKrja0S/b3XwTur2/b7yL77jGMm1XvPZ7HcIex7zzD8sDmfsYcJl3FeUt0+VET2Hq/q0l4PBD4MvCJBnBdXtw/riPd5uBWuX66qN43dCcNYKbHef/Ux/wT4QeDVONnyzuEjeqlXlrcvPmOrRH3v9fC51W37fWTffcYxE/29JyK3wJXO7nCXnp2Cfe8Zhh825zP2MOEygqq+BXghbnG/b2ztfgrO9P9s4zrql0XkU0Xk+jlxKp4DvAt4nIjcp95YfXF+d/Xnj06+c4ZROLHef9W+J+MWyX0V8AWq+q6hvkXkfiJyTcf2hwDfUv3582H3yDDWQaz3nojcU0T+Sju+iNwFl2kGh+8j++4zjpaY33sNHotbAPcFPYvlUsWy7z3D8MTmfIYvompr74xRvZFeDnws8Dzg9cD9gAfj0sEeoKrvrtpeh7t01x+p6nVT4zSOeRTuTfgR4NnAe4BH4C4f9hzgy9WeRGPDxHj/ichXAc8CzoBn0F0De6OqPqtxzEuBewIvxdW8A9wbeEj1+5NV9bsxjI0S6b13A/BPcf/xexvwfuB64IuBW+DWkvhSVb251fejsO8+40iJdd7ZiPfrwP8FPEJV/+tAvy/FvveMI6b67nlU9efHA/83Lqvr16tt71LVJ1Ztr8PmfIYHJlw8EZG/CjwVl+r1McDbgecCT2kuuDn2xecbp3XMA4HvwKVf3wJ4M/DTwNMn1uEaxqqY+/6rJn3fNdLNr6nqgxrH/F3gS3GXjb4DcBl4B/BbwA+r6q93BTGMLRHhvff5wDcAn8nFZaH/D66s7+eAn+s7gbTvPuOYiXje+Wm4BeL/BLhu6L1j33vGseNxvnj+PrM5n+GLCRfDMAzDMAzDMAzDMIzI2BouhmEYhmEYhmEYhmEYkTHhYhiGYRiGYRiGYRiGERkTLoZhGIZhGIZhGIZhGJEx4WIYhmEYhmEYhmEYhhEZEy6GYRiGYRiGYRiGYRiRMeFiGIZhGIZhGIZhGIYRGRMuhmEYhmEYhmEYhmEYkTHhYhiGYRiGYRiGYRiGEZlLuQdgGIZhGMa2EZHHAPcCXqKqv5Z7PIZhGIZhGEtgwsUwDMMwjNQ8Gbg38IbcAzEMwzAMw1gKKykyDMMwDCMZInJL4B7Vn6/MORbDMAzDMIwlMeFiGIZhGEZKPhOXUfte4C2Zx2IYhmEYhrEYoqq5x2AYhmEYxsYQkfsDvzXS7K+o6l8uMR7DMAzDMIylsTVcDMMwDMNIwV2BdwC3A24BfBD4QGP/O0y2GIZhGIaxZSzDxTAMwzCMZIjIS4HPB75BVZ+ZeTiGYRiGYRiLYWu4GIZhGIaRkntXt6/JOgrDMAzDMIyFMeFiGIZhGEYSROQuwEcDO+D3Mw/HMAzDMAxjUUy4GIZhGIaRik+vbt+sqh/MOhLDMAzDMIyFMeFiGIZhGEYqauFi5USGYRiGYRwdJlwMwzAMw0jFZ1S3JlwMwzAMwzg6TLgYhmEYhpGKOsPl1TkHYRiGYRiGkQO7LLRhGIZhGNERkVsB7wcE+Guq+seZh2QYhmEYhrEoluFiGIZhGEYKPgEnWwD+LOdADMMwDMMwcmDCxTAMwzCMFLyj8fujs43CMAzDMAwjE1ZSZBiGYRhGEkTkhcAXVn++H/gQ8GHgelXdZRuYYRiGYRjGAliGi2EYhmEYqfgK4EeAtwHXAh8HvNtki2EYhmEYx4BluBiGYRiGYRiGYRiGYUTGMlwMwzAMwzAMwzAMwzAiY8LFMAzDMAzDMAzDMAwjMiZcDMMwDMMwDMMwDMMwImPCxTAMwzAMwzAMwzAMIzImXAzDMAzDMAzDMAzDMCJjwsUwDMMwDMMwDMMwDCMyJlwMwzAMwzAMwzAMwzAiY8LFMAzDMAzDMAzDMAwjMiZcDMMwDMMwDMMwDMMwImPCxTAMwzAMwzAMwzAMIzImXAzDMAzDMAzDMAzDMCLz/wfP54nbKcZtAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = u_1.T\n",
    "\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour\n",
    "plt.figure(figsize=(20, 5))\n",
    "CS1 = plt.contourf(T, X, concatenated_array, levels=c_levels, cmap='rainbow')\n",
    "\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=24)\n",
    "plt.ylabel(\"$x$\", fontsize=24)\n",
    "plt.title(\"$u(x, t)$\", fontsize=24)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar(CS1)\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(24)  # Increase the font size for color bar tick labels\n",
    "\n",
    "plt.savefig('Contour_Exact.pdf', dpi=300, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_exact.png', dpi=300, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e5d41874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = u_1.T\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='rainbow')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=24)\n",
    "plt.ylabel(\"$x$\", fontsize=24)\n",
    "plt.title(\"$u(x, t)$\", fontsize=24)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(24)  # Increase the font size for color bar tick labels\n",
    "\n",
    "plt.savefig('Contour_Exact.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_exact.png', dpi=300, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8a9e2f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = u_1.T\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='rainbow')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=24)\n",
    "plt.ylabel(\"$x$\", fontsize=24)\n",
    "plt.title(\"$u(x, t)$\", fontsize=24)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(24)  # Increase the font size for color bar tick labels\n",
    "\n",
    "plt.savefig('Contour_Exact.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_exact.png', dpi=300, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4b10f375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = u_1.T\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(0, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='rainbow')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$u(x, t)$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "cbar1.ax.tick_params(labelsize=26)\n",
    "\n",
    "plt.savefig('Contour_Exact.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_exact.png', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1bea70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840001a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
